# What is missing in autonomous discovery: Open challenges for the community

Phillip M. Maffettone<EMAIL_ADDRESS>National Synchrotron Light Source II,
Brookhaven National Laboratory, Upton, NY 11973, USA  BigHat Biosciences, San
Mateo, CA 94403, USA  Pascal Friederich<EMAIL_ADDRESS>Karlsruhe
Institute of Technology, Karlsruhe, Germany  Sterling G. Baird  University of
Utah, Salt Lake City, UT 84108, USA  Ben Blaiszik  University of Chicago,
Chicago, IL 60637, USA  Argonne National Laboratory, Lemont, IL 60439, USA
Keith A. Brown  Boston University, Boston, MA 02215, USA  Stuart I. Campbell
National Synchrotron Light Source II, Brookhaven National Laboratory, Upton,
NY 11973, USA  Orion A. Cohen  Materials Science Division, Lawrence Berkeley
National Laboratory  Tantum Collins  Centre for the Governance of AI, Oxford,
UK  Rebecca L. Davis  University of Manitoba, Winnipeg, Canada  Ian T. Foster
Argonne National Laboratory, Lemont, IL 60439, USA  University of Chicago,
Chicago, IL 60637, USA  Navid Haghmoradi  Karlsruhe Institute of Technology,
Institute of Nanotechnology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-
Leopoldshafen, Germany  Mark Hereld  Argonne National Laboratory, Lemont, IL
60439, USA  University of Chicago, Chicago, IL 60637, USA  Nicole Jung
Karlsruhe Institute of Technology, Institute of Biological and Chemical
Systems, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen,
Germany  Ha-Kyung Kwon  Toyota Research Institute, Los Altos, CA 94022, USA
Gabriella Pizzuto  University of Liverpool, Liverpool, L69 3BX, UK  Jacob
Rintamaki  Stanford University, Stanford, CA 94305, USA  Casper Steinmann
Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg,
Denmark  Luca Torresi  Karlsruhe Institute of Technology, Institute of
Theoretical Informatics, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany
Shijing Sun  Toyota Research Institute, Los Altos, CA 94022, USA

###### Abstract

Self-driving labs (SDLs) leverage combinations of artificial intelligence,
automation, and advanced computing to accelerate scientific discovery. The
promise of this field has given rise to a rich community of passionate
scientists, engineers, and social scientists, as evidenced by the development
of the Acceleration Consortium and recent Accelerate Conference. Despite its
strengths, this rapidly developing field presents numerous opportunities for
growth, challenges to overcome, and potential risks of which to remain aware.
This community perspective builds on a discourse instantiated during the first
Accelerate Conference, and looks to the future of self-driving labs with a
tempered optimism. Incorporating input from academia, government, and
industry, we briefly describe the current status of self-driving labs, then
turn our attention to barriers, opportunities, and a vision for what is
possible. Our field is delivering solutions in technology and infrastructure,
artificial intelligence and knowledge generation, and education and workforce
development. In the spirit of community, we intend for this work to foster
discussion and drive best practices as our field grows.

##  I Introduction

Scientific experimentation and discovery is teetering on the precipice of a
new industrial revolution. Acceleration of science by combining automation and
artificial intelligence (AI) has begun to revolutionize the structure of
scientific experiments across physics Roccapriore, Kalinin, and Ziatdinov
(2022), chemistry Abolhasani and Kumacheva (2023); Shields  _et al._ (2021);
Häse, Roch, and Aspuru-Guzik (2019), materials science Tabor  _et al._
(2018a), and biology Narayanan  _et al._ (2021). The integration of high-
throughput experimentation, AI, data science, and multi-scale modeling have
spawned great interest Seifrid  _et al._ (2022), notable results Stach  _et
al._ (2021), and substantive expectations. These expectations include
acceleration of experimental throughput, new discoveries, technological
readiness, and industrial adoption. Such excitement has elicited a suite of
conferences (from a 2018 North American workshop culminating in the first
Mission Innovation report Alán, Persson, and Tribukait-Vasconcelos (2018)
coining the name Materials Acceleration Platform (MAP), to the most recent
Accelerate Conference), dedicated publication platforms, and increased funding
from governments and the private sector. Furthermore, as a link between
algorithms and the real world, self-driving labs (SDLs) are a prerequisite for
further advancements of autonomous and AI-driven research, as targeted by, for
example, the Turing AI Scientist Grand Challenge as a forward-looking
roadmapping effortKitano (2021). While this rapid community advancement may
not yet constitute a scientific revolution Shapere (1964), it does initiate a
technical revolution that will likely change the pace at which we see
scientific breakthroughs.

A self-driving laboratory (SDL) can be described as a scientific system that
performs autonomous experimentation (AE). That is, it uses automation and AI
to operate and select each successive experiment, without requiring human
intervention. Several other terms are commonly used in this domain which are
worth disambiguation from SDLs. High-throughput experimentation (HTE) applies
automation technology or engineering practices to increase data generation
rates, but these experiments are often fully designed by human experts or
predefined at the start of an experimental campaign Stach  _et al._ (2021). A
common term used for SDLs in the materials science domain is materials
acceleration platform (MAP) Alán, Persson, and Tribukait-Vasconcelos (2018).
The major differentiator between platforms is the degree of autonomy imbued in
the system—and increasingly, the degree of human-AI collaboration. In the
following, we will use the term SDL and focus broadly on (semi-) automated
platforms that accomplish (high-throughput) experiments, process and analyse
results autonomously, and use that analysis to guide future experiments. While
an SDL can be typified by a closed loop of synthesis and analysis unit
operations Burger  _et al._ (2020), a single SDL can be incorporated as a unit
operation inside a larger SDL, so long as it meets the criteria above
Maffettone  _et al._ (2022a).

Figure 1: A triptych of stable-diffusion generated images describing a self-
driving lab for autonomous scientific discovery.

Herein, we describe a community perspective on the state of SDLs, focused on
open challenges and concerns for the future. This perspective incorporates
insights from academia, government, and industry, including both users and
developers of SDLs. This work grew organically from discussions started at the
first Accelerate Conference in September 2022, and is intended to foster an
ongoing discourse to influence the field of autonomous experimentation. As
such, we have posted a first draft of this work as a pre-print to encourage
new feedback, insights, and collaboration that will shape an updated version
of the perspective. We invite interested readers to reach out to the authors
to engage in this process and to join the community discourse. While we will
first describe the current state of the field, this is by no means a
comprehensive review, and we encourage the reader toward general reviews of
SDLs and autonomous experimentation Stach  _et al._ (2021), as well as summary
articles in scientific sub-disciplines Narayanan  _et al._ (2021);
Roccapriore, Kalinin, and Ziatdinov (2022); Abolhasani and Kumacheva (2023);
Shields  _et al._ (2021); Häse, Roch, and Aspuru-Guzik (2019); Tabor  _et al._
(2018a). Following this, we will turn our attention to barriers and
opportunities associated with data, hardware, knowledge generation, scaling,
education, and ethics. As the field of autonomous experimentation grows and
SDLs become more common, we hope to see rapid growth in scientific discovery.
In the community approach to vision that follows, we look to ethically and
equitably accelerate this growth and adoption.

##  II Data, data, everywhere, and not a piece to parse

A core difference between research using SDLs and conventional research is the
amount and structure of data that are generated. Compared to human-guided
experimentation, SDLs can enable rapid experiments, with improved
reproducibility, and with automatically generated metadata. This presents many
opportunities to reduce the barrier and overheads to share, collect and use
data globally. At the same time, it leads to challenges in terms of storage,
analysis, and even interpretation of data. Perhaps one of the unique
opportunities afforded by SDLs is that data could be shared in real time with
the community while preserving its provenance. In the following, we will
discuss best practices for data sharing, but also existing barriers and
incentives to lower and overcome those barriers. Data is foundational to SDLs
and leveraging the full potential of data-driven approaches in science remains
an area of rich opportunity.

Best practices of data sharing and publication are summarised in the FAIR
principles Wilkinson  _et al._ (2016); Brinson  _et al._ (2022), i.e., data
that are findable, accessible, interoperable, and reusable. Findable means
that data should be easy to find and access, with clear and consistent
metadata, and data identifiers. Accessible means that data should be openly
available through a trustworthy and stable digital repository with a clear and
simple access mechanism. Interoperable means that data should be structured
with common standards, shared vocabularies, and standardized formats that
enable integration and reuse. Reusable means that data should be well-
described with provenance and licenses, allowing it to be used and cited in
new research and for new purposes. FAIR methods are most effective when
applied continuously during research, rather than only at the moment of data
publication Dempsey  _et al._ (2022). These principles enable the linking of
many experiments and simulations, and enable SDLs to save resources, leverage
existing knowledge, and utilize synergies. Despite the successful
implementation of FAIR principles by some exemplary practitioners Pendleton
_et al._ (2019); Blaiszik  _et al._ (2019a), there remain numerous
technological and cultural barriers to FAIR data sharing. It is thus important
to be forthcoming about these barriers, as we look to understand what
strategies or incentives are viable for improving the overall data publishing
landscape.

Primary technical barriers to entry center around data acquisition and
digitization. An early challenge is the movement of data from unit operations
to data storage infrastructure, databases, or long-term repositories Allan
_et al._ (2019); Mahnke, Leitner, and Damm (2009); Bär, Hochstrasser, and
Papenfuß (2012); Vescovi  _et al._ (2022). While electronic lab notebooks can
help integrate persistent manual processes, data acquisition in SDLs is mostly
challenged by lack of accessibility, as many scientific instruments still
produce data in obfuscated proprietary or binary formats Bär, Hochstrasser,
and Papenfuß (2012) and data often remains undiscoverable at the point of data
generation. Developing open interfaces to hardware is an ongoing and
increasingly solved task, which will enable open software to collect and
manage the data as it is produced. Data, i.e. not only characterization data
but also data regarding processes, should ideally be recorded as close to the
source as possible. If manually recorded later, this will not only increase
the effort of data collection but also potentially lead to data loss due to
incompleteness and missing standardization. Again, electronic lab notebooks
connected to electronic equipment are one way to digitize (process) data
directly during generation, while removing the effort of additional book-
keeping.

Once created, digital data can easily be stored on a hard disk as a series of
named files, yet this approach does little to ensure that the data can be
found, understood, and reused in the future by collaborators, the community,
or machines, without insight from the original data creators. This problem is
further exacerbated by the fact that most researchers are accustomed to the
file systems of their laptops, but lack the background, training, or incentive
to use shared community databases, common metadata standards, unique data
identifiers, and well-defined vocabularies Pelkie and Pozzo (2023).
Furthermore, there are additional technical challenges connected to choosing
appropriate databases (hierarchical, relational, non-relational) and data
storage with appropriate data protection, safety, and maintenance (local or
cloud). The development of research data management (RDM) tools, often driven
by bottom-up initiatives within domain specific communities, help overcome
these hurdles. RDM developments can facilitate FAIR data by providing
appropriate shared metadata standards, domain-specific vocabularies and
ontologies, and also software and storage solutions. An example RDM system
that provides some of this functionality is contained in the Bluesky project,
which implements a standard “Data API” that is exposed to users and other
systems rather than requiring knowledge of internal implementation details on
where and how the data is stored Allan  _et al._ (2019).

The last set of technical barriers relates to data interoperability, which
also extends to the homogenization of heterogeneous data sources. Relating
data from disparate equipment, including distributed instrumentation, multi-
fidelity probes, or even simulations, requires some knowledge of the core
metadata used to describe each relevant experiment. Unfortunately,
constructing these relationships requires some degree of standardization and
is highly discipline dependent. Crystallography provides an excellent example
of standardizing data formats for interoperability (protein data bank and
crystallographic information files) Berman  _et al._ (2000); Hall  _et al._
(2005). While this standardization provides a means of comparing measurements,
it does not provide rich metadata for samples. Data validation and schema
Breck  _et al._ (2019) can be used to create interoperable sample data.
Overcoming this challenge will take effort, open communication, and many
revisions. Even in a single lab, lean engineering practices should be applied
to a regularly updated and versioned data model (e.g., ambient humidity or
materials batch numbers may not be required data fields until a keen
researcher notes them as important exogenous variables). We therefore
encourage the community to develop, collaborate on, and publish data models,
particularly in a version-controlled manner. Even highly specific data models
can prove impactful and be improved upon.

An open challenge in many domains—or sub-domains—remains the creation and
support of easy-to-use, open-access domain-specific repositories and
databases. Similar to the domain-specific nature of sample data models, the
possibilities are innumerable and can start small and iterate. It is important
to note the difference between generic databases and repositories such as
FigShare and GitHub, currently only domain-specific databases provide data
with enough structure to be reused within the community Tremouilhac  _et al._
(2020). To improve discoverability, there are a number of FAIR data databases
and repositories in the materials science community that may prove useful to
collecting SDL data and for the SDL community to build upon, including the
Materials Project Jain  _et al._ (2013), AFLOW Taylor  _et al._ (2014), the
Materials Data Facility Blaiszik  _et al._ (2016, 2019b), OQMD Saal  _et al._
(2013), and NOMAD Draxl and Scheffler (2019). However, none have yet been
closely integrated with SDL. As more databases become available, a new
opportunity will be present in building infrastructure to interoperate between
them or merge them under single data models. This again relates back to
challenges with the ingestion of standardized data formats for various
hardware vendors to seamlessly relate measurements Herring  _et al._ (2020).
Quality control and continuous integration are challenging tasks even for data
workflows from a single source Apted and Murphy (2021); as interoperation
grows, so will the challenges for quality control and integration. This will
be a welcome opportunity and a hallmark of community progress. It can be
addressed by first building internal trust in data through QC pipelines that
are incorporated in a data model. Cloud tools such as AWS and Azure, as well
as workflow managers Chard  _et al._ (2023) can be used to build automation
into the construction, maintenance, and QC of these databases.

Aside from the substantial technical challenges of building a robust data
infrastructure, there are many cultural hurdles. Some are centered around
sharing proprietary data that represents a material value to its creators.
Others reflect researchers’ fear of being "scooped" or yielding a competitive
advantage. While technical issues can be solved with clever engineering and
sufficient funding, cultural issues cannot. Instead, workshops, conferences,
training programs, and higher education serve as the engines of cultural
change. We suggest conceptualizing improved data management as a socio-
technical transition pushing against both technical and cultural lock-in to
existing practices.

###  II.1 Suggestions for data sharing incentives

We have identified three strategies for incentivizing the production of FAIR
data, putting into focus the human researcher, rather than MAP technology:

  * •

Reducing friction

  * •

Providing rewards

  * •

Demanding requirements

Creating the technical means to share data in a simple way is a necessary
condition to allow researchers to share data in a sustainable way. Reducing
friction means making it simple and effortless for researchers to upload their
data to the appropriate places. Whether this is the time it takes to create a
DOI for data, or the effort needed to validate the data into a given database
schema, people will be more likely to engage with a tool if it does not feel
like work. We encourage the “customer development” required for creating an
appropriate user interface for data tools Maffettone  _et al._ (2022b). This
ties into the above discussion of creating and maintaining field-specific
databases, which are easily accessible and also integratable into SDLs to
further enable the automation of data publishing pipelines. If these are
accessible with a continually refined user interface, the community will be
more likely to contribute their data.

Even if they are easy to use, technical tools are not enough to create a
sustainable development toward openly sharing data. To encourage researchers
to share data more broadly, we believe that data sharing should be met with
recognition and rewards. The recognition could take the form of data
citations, which are tracked by the statistics of access and downloads. Such
recognition would directly create incentives to publish in databases that
impose high standards to ensure high quality, reproducibility, and
documentation of data, as such databases will be used and thus cited more by
the community. Furthermore, this can incentivize the publication of “negative-
data” Raccuglia  _et al._ (2016). Broad data availability can further lead to
citations by researchers without access to labs and HPC infrastructure.
Citable data can then lead to the definition of new metrics. Consider, for
instance, the social and professional impact of the h-index. While there are
systematic challenges associated with over-reliance on a single metric, the
h-index provides a more holistic—if flawed—means for measuring impact. We
envision a complementary metric for data (e.g., a d-index), that could be
built off of the unique identifiers for data in a database, allowing data to
be referenced digitally in publications. While new metrics bring new concerns,
integrating these metrics into the traditional advancement criteria at
research institutions would produce a dramatic cultural shift.

An alternative approach to creating incentives in the currently existing
research reward system centered around publications and citations is the use
of automated papers that are published regularly, listing all recent
contributors to a public database. This can even be transferred to track
software impact, e.g., publishing a citable list of contributions to given
repositories. This idea is not entirely new, as large software tools regularly
publish reports of new versions with all contributors as authors Harris  _et
al._ (2020). However, the main outcome of this strategy is converting data
contributions into publications/citations, and pivots the credit mechanism to
an already imperfect set of metrics. While the career advancement for a
scientific software engineer can be as closely tied to their publication
record as their git history, no such paradigm has been publicly recognized by
an R1 university, especially not in the context of data provenance. Thus, we
think creative solutions to provide micro-incentives to sharing data and
exercising good data practices will be a critical need in making progress in
this field and encourage social scientists to consider this research problem.

In continued circumstances where a reward infrastructure proves lacking, we
encourage mandates through peer review and funding agencies. This policy can
be enacted at the journal or editor level, or be enforced at the level of
individual peer review, as FAIR data are a reasonable pre-requisite for
reproducibility. Some journals already enforce code review as part of their
process for scientific software, and it is sensible to enforce data review for
papers that describe large datasets. With regards to funding, many government
funding agencies require a data plan, albeit, enforcement is critical.
Furthermore, data plans often only include positive results. Public access to
data created using public funds is paramount and should be rigidly enforced by
funding bodies. Data plans, however well-designed, are not useful if they are
not used. There is also no reason why this must be done from scratch: for
example, a data management framework could be developed and highlighted
alongside a new dataset. Best practices will naturally differ between
communities with different measurement techniques, instrumentation, and
figures of merit. It is therefore advisable that data management frameworks be
designed through active collaboration between scientific communities and
funding agencies.

##  III Integrating hardware into SDLs

The experimental apparatus that constitutes the physical embodiment of the SDL
provides its own set of challenges. While data and software solutions are
reusable across a large swath of the research landscape, hardware advancements
must be capable of handling the specifics of the research problem at hand.
Further, physical platforms are generally the most capital-intensive part of
an SDL, including the costs of the scientific instrumentation required for a
given experimental workflow, and often custom automation hardware. Therefore,
we stress an objective that the hardware powering SDLs generalise across
different experiments to make the investment reasonable.

There are three common approaches to hardware in current SDLs: building
hardware from individual components (e.g., motors, pumps, controllers,
detectors); using workcells (i.e. integrated systems which bring together
automation equipment, analysis tools, and software, to accomplish rigid
predefined tasks); and integrating unit operations with an anthropomorphic
robotic platform. The from-scratch approach is most common in specialist
equipment, such as beamlines Maffettone  _et al._ (2022b); Allan  _et al._
(2019), new microscopes Hohlbein  _et al._ (2022), or specialist synthesis
approaches Tabor  _et al._ (2018b); however, growing maker communities have
driven some build-your-own workcells similar to 3D printer technology Vasquez
(2019). Workcells first came to use in high throughput biological applications
King  _et al._ (2009), and now have commercial providers across the physical
sciences. These are relatively rigid unit operations for a given experiment
type, although it has been demonstrated that a robotic arm can be integrated
internally which could provide more flexibility MacLeod  _et al._ (2020). To
add even more flexibility, the use of mobile robots for transporting sample
vials and using equipment across different laboratory stations has been
demonstrated Burger  _et al._ (2020), in addition to the usage of
heterogeneous robotic platforms depending on the laboratory tasks Fakhruldeen
_et al._ (2022). As such the current state of the art for laboratory
automation varies depending on the commonality of the task to automate, and
the commercial demand for products; a generic liquid handler has a much
broader market appeal than a domain-specific workflow.

Although building hardware is hard, it pales in comparison to the challenge of
integrating hardware. This has amassed public Bär, Hochstrasser, and Papenfuß
(2012); Quigley  _et al._ (2009); Allan  _et al._ (2019); Pendleton  _et al._
(2019); Fakhruldeen  _et al._ (2022) and private efforts. Developing a
hardware approach for an SDL involves a combination of deciding on the best
tools for the laboratory task and building common interfaces for those tools.
As an example, we contrast a synthesis workflow that can be accomplished in a
commercially available workcell or using custom equipment, an analysis
workflow that depends on advanced detectors, and a sample management workflow
that uses robotic arms. The synthesis workcell may include a software
interface that cannot be rebuilt, whereas the custom synthesis equipment will
need to choose an effective software interface. Advanced detectors on the
other hand are commonly integrated using open software tools Dalesio, Kozubal,
and Kraimer (1991); Pinter and Yoaz (1996). Lastly, robotic arms are
traditionally driven using middleware that, through already available
libraries, have in-built motion planning, low-level controllers, and
perception Quigley  _et al._ (2009). While these are fundamental for
autonomous robotic platforms, given that most synthesis or analysis workflows
are carried out in open-loop, these functionalities have not been fully
exploited. This then raises the question of how to best integrate these
disparate systems without attempting the mammoth task of rebuilding them all
using a single software tool.

We encourage the development and reuse of open-source, non-proprietary
hardware communication, as well as interfaces between those common platforms.
This would facilitate sample exchange across multiple commercial experimental
tools, in conjunction with bespoke tooling, as well as improving knowledge
transfer, reproducibility, and generalization to new SLDs. Interfaces should
make use of industry-standard message bus technology, that will enable both
local and cloud operation. Moreover, these interfaces are compatible with
streaming data, that enable _in situ_ and real-time measurements with
automated data-processing pipelines. The materials community has called for
investment in “the redesign of microscopes, synchrotron beamlines, and other
sophisticated instrumentation to be compatible with robotic sample
handling—akin to the multi-plate-handling robots in the bio-community.”Stach
_et al._ (2021) Such innovation would be empowered by the adoption of open
frameworks and message buses (e.g., by integrating a ROS-enabled robot, a
Bluesky-enabled beamline, and SILA-enabled unit operations).

The economy of public research requires that SDL platforms—or at least their
components—be reusable beyond the scope of a single research project. One
approach to this is to develop modular systems that can be added to over time
to meet the new demands of new research questions. This would be supported by
a common physical sample interchange environment. The details of sample
interchange greatly depends on the form factor of the material being measured.

Samples can generally be clustered into three varieties: liquid, bulk solids
(including thin films on substrates) and powders. Liquid handling is the most
common in current SDL setups, having been pioneered by the bio-pharmaceutical
sector. Samples can be physically handed between instruments in vessels, moved
through the use of pipetting, or directly pumped through piping. Handling of
bulk solids is also relatively straightforward. Robotic arms are able to move
samples between processing and characterization tools. The sample surface is
also readily available in this form factor for characterization. Each sample
may be homogeneous or contain multiple sub-samples. Powders are perhaps the
most challenging to deal with using automation. While precursor powders can be
readily dispensed, powders as a product can be difficult to handle. While
powders can be moved in a vial, this is often not a form factor that is
amenable for characterization. For example, X-ray diffraction typically
requires creating a flat surface or packing into a capillary. Conversely,
powders for catalysis or absorption of gasses typically require these powders
to be packed into columns for testing. Further complicating this process is
the fact that powders can have a great variety of flow properties, requiring
adaptive manipulation. We encourage robotics and mechatronics research in this
area, as automation of powder handling would solve a particularly impactful
tactile challenge.

###  III.1 Applying Robotics to SDLs

Deploying anthropomorphic robotic systems in SDLs is a promising area of
research in that it enables the use of existing or standard instrumentation,
empowers human collaboration for non-automated tasks, and is generally more
flexible MacLeod  _et al._ (2022a). Despite the increased cost and complexity,
there have been several research efforts in this area, spanning applications
from autonomous solubility screening MacLeod  _et al._ (2020); Pizzuto  _et
al._ (2022); Fakhruldeen  _et al._ (2022), photocatalysis Burger  _et al._
(2020), and automated synthesis Lim  _et al._ (2021). SDLs provide an exciting
semi-structured environment where the robotics community can transfer their
methods to novel applications. Robotics researchers have focused their
attention on various applications—from household environments to extreme
environments such as nuclear and space—that possess common underlying
challenges with SDLs. For example, the challenges of assistive home robots
related to grasping transparent glassware are also present in SDLs. Towards
this goal, learning-based methods e.g. TranspareNet Xu  _et al._ (2021) and
MVTrans Wang  _et al._ (2023) have been demonstrated for detecting laboratory
glassware. In addition, there exists the need to have more task-specific
grippers, such as grippers that are specifically designed for laboratory
containers and well-plates Zwirnmann  _et al._ (2023). While a universal
gripper that would exhibit the dexterity of a human hand may seem ideal, we
are still quite far from this and most laboratories have to either adapt their
current grippers with 3D printed parts and/or use tool exchangers.
Alternatively, laboratory tools e.g. pipettes can be made more robot-friendly
Yoshikawa  _et al._ (2023). Amongst others, these approaches will pave the way
to having more robust laboratory robotics for experiments over long periods of
time.

While there is an increasing interest towards using anthropomorphic robotic
platforms, primarily due to their attractive nature of being deployed in human
labs, there still exist a large number of challenges with having these
platforms carry out long-term experiments. As a robot can be described as any
system of sensors and actuators that can perform a complex task, it is worth
noting when a workcell or other automated unit operation can be used to
substitute or supplement an anthropomorphic robot. To this end, modular
systems that can—in principle—handle a wider range of experiments due to the
closed nature of their subsystems have been demonstrated as alternatives
Steiner  _et al._ (2019); Manzano  _et al._ (2022). To date, the SOTA in using
anthropomorphic robotic systems in SDLs have carried out experiments on open
benches, which limits the generalisation of experiments towards materials that
have a higher degree of toxicity.

Democratization of laboratory automation is a crucial path for the
contemporary developments of individual lab groups to transcend beyond local,
bespoke solutions. It has thus far been exceptionally difficult for the
community to transfer knowledge between laboratories regarding their hardware
development and integration. We are calling for a focus and investment in
modularity and open hardware, that makes use of the aforementioned
communication approaches. Publishing modular hardware components—either
through new journals or maker spaces—will enable the community to take
advantage of rapid prototyping and manufacturing. The combination of open
hardware and open control software will have the accelerating effect of
democratizing access to lab automation and ensuring our brilliant peers have
this technology regardless of resources.

###  III.2 Miniaturization

Notably, we see multiple opportunities in miniaturization and reducing the
footprint of hardware to both reduce the barrier to entry and reduce material
consumption and waste production. Digital microfluid platforms have a high
capability for miniaturization because they replace moving mechanical parts
with a liquid that needs to be moved Soldatov  _et al._ (2021). The HTE
community in biology and chemistry has long used microwell plates to reduce
sample volumes to the microliter scale Hertzberg and Pope (2000); Battersby
and Trau (2002). In materials science and chemistry, miniaturization can have
a high impact Potyrailo and Amis (2012); Karthik, Kasiviswanathan, and Raj
(2016) but is to date underexplored. In addition to adopting microwell plates
Buitrago Santanilla  _et al._ (2015), microfluidic reactors allow for samples
in SDL to reach the nanoliter scale Epps  _et al._ (2020). SDLs built entirely
using flow chemistry can take advantage of established chemical engineering
without anthropomorphic robots. Flow reactors are also easier to integrate
with online characterization techniques Abolhasani and Kumacheva (2023).
Milli- or micro-fluidic reactors have the advantages of higher rates of heat,
mass, and photon transfers as a result of the enhanced surface-to-volume
ratio. Nonetheless, flow reactors suffer from the possibility of clogging when
dealing with solid-state materials or precipitates Delgado-Licona and
Abolhasani (2023). These cases (including thin-film preparation, battery
materials, or polymerization with precipitation of solid products or
byproducts) are better suited for parallel batch reactorsAbolhasani and
Kumacheva (2023). Modular microfluidic units could accelerate process
optimization and formulation discovery; however, a standardized protocol in
modular configuration for a targeted reactive system needs to be established
Volk  _et al._ (2022).

In addition to the application of microfluidic reactors, miniaturization has
also been applied to devices and solids sample arrays. This has been
approached using combinatorial synthesis in small areas (<1 mm2)Takeuchi,
Lauterbach, and Fasolka (2005), multinary thin film synthesis Ludwig (2019);
Nikolaev  _et al._ (2014), and microdroplet array synthesis Feng, Ueda, and
Levkin (2018); Rosenfeld  _et al._ (2020); Seifermann  _et al._ (2023). Recent
work with scanning probes has shown that sub-femtoliter solutions can be
patterned and combined on surfaces, providing further opportunities to
miniaturize experiments Saygin  _et al._ (2021); Brown (2022). Such samples
can also serve as miniaturized reactors Zech, Claus, and Hönicke (2002),
having the broad effects of reducing material consumption and increasing
experimental throughput. In addition to miniaturizing samples, it is also
powerful to minimize the analytical instrument to study these samples, as
exemplified by scanning droplet cells to study corrosion Lohrengel, Moehring,
and Pilaski (2000) and adapting electrochemical characterization techniques
Jenewein  _et al._ (2022); Zhang  _et al._ (2020). While not all
characterization techniques can be applied in miniaturized platforms
Abolhasani and Kumacheva (2023), we encourage further developments in this
area that increase automated capacity while reducing material consumption.

##  IV Algorithms

###  IV.1 Autonomous decision making for optimization problems

The traditional design of experiments (DoE) becomes rapidly impractical for
high dimensional problems due to the exponential growth of the number of
required experiments. Incorporating ML in SDLs has emerged as an efficient way
to explore the chemical space and to speed up experimentation. Taking
advantage of the information generated during the optimization process itself,
ML enables an iterative experimental design that maximizes the information
gained per sample and that requires a smaller number of experiments with
respect to traditional DoE Delgado-Licona and Abolhasani (2023); Roch  _et
al._ (2020).

At present, a variety of ML approaches have been applied to SDLs. Genetic
algorithms (GA) are a class of adaptive heuristic search algorithms inspired
by the process of natural selection that can be used for solving both
constrained and unconstrained optimization problems. GAs have been applied
recently to optimize the conditions to produce gold nanoparticles Salley  _et
al._ (2020). Reinforcement learning (RL) has also been successfully applied in
SDLs Rajak  _et al._ (2021); Li  _et al._ (2020); Maffettone  _et al._ (2021).
This is an ML paradigm that enables an agent to learn through trial and error
in an interactive environment by taking actions and receiving rewards, with
the goal to learn a policy that maximizes the total reward over time. The most
widely used decision-making algorithm in SDLs is Bayesian optimization (BO), a
method particularly suited to balance the trade-off between exploration and
exploitation of the input parameter space. BO has been applied to SDLs both in
the single objective setting MacLeod  _et al._ (2020); Gongora  _et al._
(2020); Shields  _et al._ (2021) and, more recently, for the simultaneous
optimization of multiple objectives MacLeod  _et al._ (2022b). There are two
main strategies for the implementation of multi-objective BO: combining
multiple objectives into one (e.g., Chimera Häse, Roch, and Aspuru-Guzik
(2018)) and identifying a Pareto front that trades off among the multiple
objectives (e.g., qNEHVI Daulton, Balandat, and Bakshy (2021)). The second
approach has the advantage of not requiring the experimentalist to select the
trade-off among the different objectives _a priori_.

A challenge in SDLs is selecting suitable algorithms for each specific
scenario. Open-source software packages for SDLs offer an easy-to-use starting
point for non-experts in machine learning to begin autonomous experimentation
(ChemOS Roch  _et al._ (2020), EDBO+ Shields  _et al._ (2021); Torres  _et
al._ (2022)). Other general-purpose libraries are becoming more user-friendly
and are constantly updated with SOTA methods, such as BoTorch Balandat  _et
al._ (2020), or Ax, an adaptive experimentation platform built on top of
BoTorch. Often, off-the-shelf decision-making algorithms in general require
further tuning, which can slow research and even undermine an
experimentalist’s intent in purpose of applying them. In this sense, the lack
of open-access datasets for experimental campaigns is a current issue.
Relating back to our discussions around data (Section II), the availability of
data would allow researchers to evaluate novel algorithms on multiple
surrogate systems based on real experiments, while the absence of such
datasets could considerably impede the development of dedicated algorithms and
create obstacles for the development of autonomous platforms Liang  _et al._
(2021); Epps  _et al._ (2021).

An open challenge is the incorporation of generative models Sanchez-Lengeling
and Aspuru-Guzik (2018); Gómez-Bombarelli  _et al._ (2018); Noh  _et al._
(2020) in SDLs. While being very successful in finding hypothetical molecules
with tailormade properties, i.e. solving the inverse problem of materials
design, generative models frequently suggest molecules with complex or unknown
synthesis routes Gao and Coley (2020); Bilodeau  _et al._ (2022), which limits
their real-world impact and prevents their application in automated labs.
Including synthesizability or even synthesis planning in generative models for
inverse design, as well as developing versatile multi-objective generative
models is a promising path toward their integration in SDLs Gao, Raghavan, and
Coley (2022); Bradshaw  _et al._ (2019).

A further open, yet more technical challenge in self-driving labs is the
systematic exploitation of existing data and also process descriptions Kearnes
_et al._ (2021) that are published in the literature. Automatic extraction of
that data and conversion from natural, i.e. informal language to computer-
readable, i.e. formal language is an open challenge, currently requiring a
large amount of manual work Luo  _et al._ (2022). Large language models can
potentially help in that task Gupta, Zaki, and Krishnan (2022); Dunn  _et al._
(2022) but further research is required to reliably extract data and knowledge
from scientific literature.

###  IV.2 Knowledge Generation

The ultimate goal of scientific experiments is typically not (only) the
generation of data, but the generation of knowledge and understanding Krenn
_et al._ (2022). From that aspect, the main objective of SDLs should go beyond
solving optimization problems in high dimensional spaces of materials and
processing conditions. Rather, they should aim to interpret that data, link it
with other data (potentially from other SDLs and databases), and help to
generate and test scientific hypotheses (potentially in a semi-autonomous or
autonomous manner Friederich  _et al._ (2021)). To come closer to the goal of
autonomous generation of scientific knowledge and understanding,we propose
multiple considerations for the future.

Firstly, in order to leverage data generated across labs and use it to train
models, further data analysis, and generate new scientific knowledge, we
underscore on the efforts highlighted in Section II to link data through
shared data formats, metadata definitions, vocabularies and ontologies. This
will move SDLs beyond the discovery of single, interesting data points. From
these innovations, data can be subsequently used for transfer learning models,
multi-fidelity and multi-task models, as well as representation learning
methods, which can learn from heterogeneous datasets Khoury, Baliban, and
Floudas (2011). Such pre-trained models enhance the decision-making process in
SDLs. In particular, they not only learn from locally generated data but
already have prior knowledge that enhances decision-making early in an
experimental campaign. Lastly, we encourage the development of AI/ML methods
for SLDs that reach beyond optimization problems. SDLs are very good at
finding optima, which can act as sources of inspiration for scientific
understanding, as defined in Krenn et al. Krenn  _et al._ (2022). Furthermore,
increasingly sophisticated explainable and self-explaining machine learning
models for molecules Ying  _et al._ (2019); Teufel  _et al._ (2022), materials
science Oviedo  _et al._ (2022); Zhong  _et al._ (2022) and particularly SDLs
Pilania (2021); Kailkhura  _et al._ (2019) pave ways towards autonomous loops
of hypothesis generation. Methods such as automated generation of
counterfactuals Wellawatte, Seshadri, and White (2022) offer further
opportunities for automated hypothesis testing, when combined with fully
automated synthesis and characterization, or accurate predictive simulation
workflows.

##  V Scaling autonomous discovery

Taking the opportunities of data, hardware, and software in concert, we can
turn our attention to economies of scale. In this section, we focus our
discussion on scaling via interconnection of multiple distinct hardware
modules and SDLs Maffettone  _et al._ (2022a), or via increasing the size,
capacity, or extent of a given SDL. The scalability of SDLs supports a faster,
more efficient exploration of experimental parameter space, as well as a
larger volume output of manufacturing processes.

SDLs at the laboratory scale already empower HTE increasing throughput by
orders of magnitude. Scaling an SDL beyond the single lab will increase
experimental throughput proportionately. Moreover, moving from local autonomy
to distributed autonomy will enable optimization and search over multiple
length scales, characterization methods, and related systems. For example, a
lab-scale SDL within a single confined system, such as glove box handling of
liquids, may enable a higher experimental throughput. Coupling this with an
SDL at a different scale, such as a synchrotron, may enable new discoveries by
bridging techniques across multiple length scales. Scalable computational
approaches can capitalize on a greater experimental throughput and the
interplay between modular SDLs for a more efficient search through space.
Designing SDLs that act in concert over multiple fidelities and length scales
will enable materials verification and validation that is nearer to industrial
requirements. This ties in with the “advanced manufacturing” movement that has
received significant attention in the last decade Cheng  _et al._ (2018).

Building scalable SDLs requires a particular landscape of considerations.
Broadly these include the manufacturing considerations and the transition from
automation to local autonomy to distributed autonomy. There are challenges in
scaling the volume of experiments, synchronizing data (Section II), algorithms
for handling multifidelity and multimodal data (Section IV), and software that
enables distributed orchestration across platforms. As the number of
experiments performed per unit of time increases, it is crucial that
experimental platforms are found that minimize the amount of material required
per experiment (Section III). This reduces the costs of experiments, makes
them more amenable to parallelization, and reduces the time required for some
types of processing steps. However, this may change the relevance to
manufacturing scale, so there is a case to be made for multi-scale automation
that features high-throughput experiments at a highly miniaturized scale and
lower-throughput experiments at larger scales.

Scaling beyond single-laboratory SDLs creates some open questions in cost
analysis. How do we model the cost of implementing and operating such a large-
scale high-throughput discovery architecture? Can we quantify the aspects of
scale that result in cost efficiency? The capital expenses would include floor
space, instrumentation (sample production, characterization, storage), and
mechanical infrastructure (robotics, table space, resource garages). The
operational expenses would include materials, power, maintenance, and
replacement. There are broad considerations around architectural design, and
efficiently accommodating the disparate instrumentation sets required by
different workflows. We encourage such engineering and feasibility analyses by
public research centers to be made broadly available, to inform continual
improvement cycles Deming (1982). Calling back to our discussion on
modularity, we further highlight the economic analyses of “platform” (i.e.
incremental) vs “bespoke” (i.e. single-leap) development strategies for large
projects, and resoundingly encourage the former Ansar and Flyvbjerg (2022).

When considering the transition from automation to local autonomy to
distributed autonomy, we are focusing first on the distinction between HTE and
AE/SDLs, with a second distinction between an isolated SDL and a distributed
SDL or network of distributed SDLs Maffettone  _et al._ (2022a). Mobility may
be a key component to making the transition from local to distributed
autonomy. As discussed in Section III, this can be accomplished by fixed
material transport systems (robotic rail systems, custom feeders) or by more
generalized mobile robotic technology.

Large-scale autonomous experimentation also creates two opportunities for
software innovations. Scaling SDLs will require actional workflow
systems—mediated perhaps by powerful workflow languages—designed to enable a
description of the requirements for each sub-task in a workflow as well as the
specifics needed to connect intermediate products from one sub-task to the
input of subsequent sub-tasks. Intelligent search or orchestration that scales
is a second opportunity for software innovation in an ongoing area of rich
study. This requires a capacity for evaluating incoming results from
experiments carried out in parallel on the work floor, and converting this new
data into new experimental queries. This computational component connects the
stream of outputs from in flight experiments back around to the process that
injects new experiments into the available resource pool. It does so in light
of all previously collected experimental data, models of the processes being
studied, and a growing model of the abstract landscape defined by the problem
goal specification.

##  VI Education

A large span of educational backgrounds is currently required to drive the
development of SDLs, ranging from traditional sciences, technology,
engineering, and math (STEM) to humanities. This raises the question of how to
approach educating the next generation of students and researchers so that
they are prepared to develop and responsibly use automated and self-driving
labs. With this section, we hope to promote discussion amongst educators as to
how to integrate the ideas and techniques of SDLs into a curriculum and
provide resources that will enable such development. This section is organized
into three subsections, (1) the topics that researchers in this field should
know, (2) mechanisms for teaching them, and (3) thoughts on how to assess
success. On a larger scale, we hope to spur discussion as to what should be
considered foundational knowledge in higher level education and how we enable
future generations of scientists to contribute to and advance these growing
sectors of research and discovery.

###  VI.1 Fundamental knowledge underpinning AI-accelerated science

As we have already discussed in great detail, SDLs bridge a broad range of
topics such as AI, computing, engineering and automation of experiments.
Prospective scientists entering the field of acceleration will typically have
one domain of expertise but maybe little to no experience in other domains.
Thus, it may be challenging for an individual to master all skills necessary
in addition to their experience in their application area. The
multidisciplinary nature of the field poses challenges to educators in both
deciding on the prerequisites and lecturing for an audience with diverse
academic backgrounds and research interests.

The intellectual barrier to entry into AI-accelerated research involves
fundamental theoretical knowledge as well as practical skills, both of which
must be acquired through training and practice. Specifically, practitioners
must understand the fundamental topics of:

  * •

how and when to conceive of scientific research as an iterative workflow,
involving the selection and performance of experiments together with the
subsequent analysis of results,

  * •

how one can go from an existing gap in knowledge to defining critical
bottlenecks that limit the speed at which that gap can be filled,

  * •

the mathematics and computer science underpinning SDLs, e.g. statistics,
probability theory, linear algebra, programming, automated data-analysis,
basic machine learning and automated planning/decision-making,

  * •

lab automation including existing solutions, modular setup of SDLs,
communication protocols of automated equipment,

  * •

and the history of AI- and automation-accelerated research.

  * •

lab automation including existing solutions, modular setup of SDLs,
communication protocols of automated equipment,

  * •

and the history of AI- and automation-accelerated research.

In addition, practitioners should have the following skills:

  * •

data management and curation

  * •

algorithmic data processing including scripting the extraction, analysis, and
presentation of large datasets from possibly heterogeneous sources.

  * •

interdisciplinary teamwork combining software, hardware, and domain experts

  * •

and fluency across these disciplinary intersections for effective
communication with diverse researchers

###  VI.2 Mechanisms for training in AI-accelerated science

There are a number of approaches to learning and teaching about AI-accelerated
science that can appeal to a wide array of educational backgrounds, experience
levels, and time commitments. We envision a multi-faceted approach to training
new students and existing researchers in SDL-accelerated research. Here we
provide a list of a few such facets that are organized from lowest barrier to
entry to those that require more time, expertise, and resource commitment.

  * •

Freely available videos are a great resource for beginners and the community
would benefit from repositories of such videos that allow learners to sort and
search to find topics of interest among curated or trusted videos. Such
methods have been used systematically in engineering education.Lindsay and
Morgan (2021)

  * •

For students excited about active participation, workshops at conferences are
an excellent resource, especially those that are paired with large meetings.
For instance, the MRS Data Science Tutorial Organizers hosted two machine
learning competitions in recent years, one in fall 2021 focusing on active
learning and another in fall 2022 on supervised machine learning.Sun, Brown,
and Kusne (2022)

  * •

More formal courses can be helpful for some learners and be longer and more
substantial than workshops and tutorials. The collaborative nature of the AI-
accelerated materials community raises the possibility of jointly developing a
course that can have a presence at multiple universities and touch on many
material or chemical domains. One example of how such a course may be
developed collaboratively by many researchers comes from the area of
computational chemistry, where a shared course has been developed between
multiple institutions.

  * •

As a powerful resource for either advanced users of instructors developing
curricula, shared digital resources such as datasets and code can be directly
shared. While these often require a certain degree of expertise to
incorporate, they provide access to powerful techniques. However, it is
necessary to efficiently share these with attribution. One avenue for doing
this are repositories of materials informatics resources such as REMI:
REsource for Materials Informatics111https://pages.nist.gov/remi/.

  * •

Student internships in companies that develop robotics hardware and software
as well as companies that have long-standing experience in advanced automation
will help to transfer existing industry know-how into academic environments
and apply it to advance SDL technology.

  * •

As in many fields, there is no substitute for learning with hands-on
experience. SDLs provide some unique opportunities for such hands-on learning.
Suitable cost-effective SDLs Baird and Sparks can be adopted by instructors
for teaching settings. Alternatively, enthusiastic learners can directly
leverage these resources to learn independently. Furthermore, students should
be incorporated in existing and currently developed SDLs through thesis
projects or research internships, to be exposed to SDL technology as early as
possible, and also to transfer knowledge and know-how between labs.

A number of these examples show the value of openly sharing software,
hardware, and data. As such, we view this as a strong encouragement to
continue and expand the practice for education in AI-accelerated research.

###  VI.3 Assessment of Educational activities

It is important to evaluate the effectiveness of coordinated training and
education efforts in the community. Moreover, the insight from these
assessments should be shared to collectively improve practices. While there is
no single metric that can address all facets of education activities, the
following avenues stand out as promising processes to gather and act on
feedback.

  * •

Workshops and tutorials can be evaluated by quantifying attendance, soliciting
feedback, and student outcomes. While attendance itself is not especially
important as different venues lend themselves to different scales, the
fluctuation of this quantity over time can speak to the impact and reputation
of the event. Feedback on educational events should always be solicited
following the event when thoughts are still fresh in the minds of the
attendees. This can be through informal short surveys administered online. For
classes with certificates, filling out feedback can be made a prerequisite for
receiving the certificate. Actual learning outcomes are more challenging but
more important to measure. The key question is whether the educational efforts
led to research action. While open to confirmation bias, a starting point
would be to interview successful students to learn what facets of their
education were most effective.

  * •

Open-source educational resources can be assessed through the degree to which
they are accessed. Basic analytics can help determine how many new users are
present and how long these users spend with the resources. Such results can
help guide the refinement of further resources. That said, there is a
difference between useful, popular, and correct, which means that raw user
numbers do not tell the whole story. It would be useful to couple such metrics
with expert-curated recommendations to highlight effective resources.

  * •

One avenue that has already been impactful in machine learning and computer
science is the use of competitions. In addition to generating excitement about
the field, these can serve as an avenue for assessment in evaluating the
results from typical participants. In addition to competitions with a broad
scope, in-class competitions have several advantagesGamarra  _et al._ (2021),
including encouraging learning by doing and providing an easy-to-implement
platform for evaluating student progress.

Just as self-driving labs represent an iterative cycle in which experiments
are performed and the outcome is used to learn and choose subsequent
experiments, it is important to view pedagogy along the same lines and use
assessment to build on and improve past efforts. This reflects the dynamic and
evolving nature of this field and our expectation that it will grow and evolve
in the years to come.

##  VII Ethics and Community

Research on and with autonomous experiments could have a powerful impact on
society. A development and deployment process that does not include careful
planning, broad consultation, competent execution and ongoing adaptation might
create long-term harms that outweigh SDLs’ benefits. Although anticipating all
potential complications is impossible, exploring possible problems—as well as
solutions and mitigations—early and frequently could reduce the expected cost
of such issues. The space of ethical considerations relevant to SDLs is too
broad to canvass comprehensively here, but this section highlights a few key
categories.

First, by lowering the cost and increasing the accessibility of scientific
R&D, SDLs could profilerate destructive capabilities as well as research
progress. For example, these facilities might enable actors with malicious
intent to develop hazardous materials of biological, chemical and nuclear
origin. Research has already shown that relatively simple machine learning
methods can generate novel toxins that are potentially more fatal than
previously-known substances and that do not feature on chemical controls and
watch lists, creating new governance challenges Urbina  _et al._ (2022). SDLs,
if not managed carefully, could enable further experimentation along these
lines and possibly the large-scale production of dangerous substances Hickman
_et al._ (2022). SDL governance will have to balance responsiveness to these
concerns with sensitivity to researcher privacy. Physical and cyber security
will also play a critical role, since poorly-secured labs, regardless of the
soundness of their governance, could be vulnerable to hijacking by hostile
actors.

Second, neglect and AI safety failures could lead to risks similar to those of
malicious intent. Equipment malfunctions, insufficient cleaning and
maintenance, poor storage practices and so forth might inadvertently create
harmful substances, for instance by contaminating a procedure that would
otherwise be safe. This issue is of course not unique to SDLs—is a general lab
safety concern—but the absence of regular human supervision removes a critical
auditing layer. Relatedly, the increased role of automated systems in SDLs
raises the importance of addressing AI safety issues: a powerful, unaligned
system prone to misinterpreting user requests or unfamiliar with a
comprehensive range of lab safety practices, standards and risks could, given
access to a well-stocked scientific facility, do tremendous damage by, for
instance, mixing volatile substances or developing and dispersing toxins or
pathogens Turchin and Denkenberger (2020); O’Brien and Nelson (2020) These are
among the scenarios that most concern AI safety researchers Koehler and Hilton
(2022); Hilton (2022).

Third, SDLs, like prior automation, could have adverse social and political
consequences. Historical parallels from the industrial revolution to the
recent rise of the gig economy show that these costs can include unemployment
and underemployment, reduced mental health, a sense of diminished community
and security, and inequitable economic impacts. These problems in turn can
trigger escalatory cycles of political backlash, and can result in regulation
that slows technical progress. To minimize the likelihood and impact of such
dangers, the SDL community should not only study technical aspects of the
technology, but also investigate adjacent social systems and relevant
historical precedents. To this end, SDL developers should partner with
economists, historians, social activists and stakeholders likely to affected
by the development and deployment of these technologies. However, given the
impossibility of perfectly predicting complex social systems, individuals
working on SDLs should also prioritize ongoing monitoring of and adaptation to
unexpected developments.

Fourth, as a form of economic activity that involves industrial components and
processes, SDLs could have negative environmental impacts. For instance, a
rise in the use of heavy machinery as the costs of experimentation drop might
raise carbon emissions, and increasing chemical R&D might damage local
ecosystems. Guarding against these risks will, like addressing social and
political consequences, require a combination of foresight (in this case,
making the most of the growing environmental science literature) and
responsiveness to unexpected developments. Unlike social and political risks,
however, environmental issues stand to benefit fairly directly from the
research that SDLs could enable Pyzer-Knapp  _et al._ (2022); Giro  _et al._
(2023). We encourage the SDL community to make climate-related topics a top
area of investigation.

Fifth, even if none of the above risks transpire, SDLs could cause harm via
incurring inequitable impacts, for instance by concentrating economic gains,
additional research prestige, etc. amongst privileged groups. We encourage
building working environments that promote equity, support diversity and
require inclusion. As a uniquely interdisciplinary community, we celebrate the
strengths that derive from differences. In creating working environments where
all are welcomed, valued, respected, and invited to participate fully, we will
accelerate SDLs’ positive impact. The community should incorporate equity and
justice in the selection and implementation of education, research,
development, policy, and commercialization. This includes openly distributing
the results of early-stage research and development in line with FAIR
practices, as well as continued commitment to ethical and reproducible
research.

Identifying risks does not solve them, but it represents an important first
step. Ideally, SDLs could create products and processes that actively counter
these dangers, e.g. by enabling people to concentrate on the safest, most
enjoyable aspects of discovery, contributing to climate change mitigation and
adaptation, and making scientific knowledge and experimentation more equitable
and accessible. However, this will not happen without active guidance;
realizing this vision will require a concerted effort from the SDL community.

##  VIII Conclusion

The field of AE and SDLs has the potential to power a new revolution in the
pace and nature of scientific discovery. As with any revolution, the community
shapes the process and outcome. At this pivotal nascent moment, we acknowledge
that there are rich opportunities at every intersection of our community, from
software to hardware to education and ethics. It is crucial that we take
deliberate action to ensure that our collective progress as fruitful and
positive as possible. Important considerations that must be addressed include
how we acquire, store, manage, and share our data, as well as how we develop,
disseminate, and scale our hardware and software solutions. These innovations
do not happen in a vacuum and, as such, we have also highlighted the ethical
implications of the field and future education and community needs. Born out
of a diverse discourse and community feedback, we hope this perspective will
provide guidelines, encouragement, and facilitate community building.

##  IX Acknowledgments

We are grateful to our peers for their gracious and varied contributions to
this work. As many ideas and pieces were shared across various media, the
organizing authors have been listed first for correspondence, followed by an
alphabetical list of all other contributors.

We would like to also thank Howie Joress for his insightful discussions.

P.M. used resources of the National Synchrotron Light Source II, a U.S.
Department of Energy (DOE) Office of Science User Facility operated for the
DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-
SC0012704., and resources of a BNL Laboratory Directed Research and
Development (LDRD) project 23-039 "Extensible robotic beamline scientist for
self-driving total scattering studies". P.F. and N.H. acknowledge support by
the Federal Ministry of Education and Research (BMBF) under Grant No.
01DM21001B (German-Canadian Materials Acceleration Center). P.F. and L.T.
acknowledge support by the Federal Ministry of Education and Research (BMBF)
under Grant No. 01DM21002A (FLAIM). C.S. acknowledges financial support from
VILLUM FONDEN (Grant No. 50405).

##  X Disclaimer

These opinions, recommendations, findings, and conclusions do not necessarily
reflect the views or policies of NIST or the United States Government.

Certain equipment, instruments, software, or materials are identified in this
paper for informational purposes. Such identification is not intended to imply
recommendation or endorsement of any product or service by the authors or
their respective institutions, nor is it intended to imply that the materials
or equipment identified are necessarily the best available for the purpose.

## References

  * Roccapriore, Kalinin, and Ziatdinov (2022) K. M. Roccapriore, S. V. Kalinin, and M. Ziatdinov, “Physics discovery in nanoplasmonic systems via autonomous experiments in scanning transmission electron microscopy,” Advanced Science 9, 2203422 (2022).
  * Abolhasani and Kumacheva (2023) M. Abolhasani and E. Kumacheva, “The rise of self-driving labs in chemical and materials sciences,” Nature Synthesis , 1–10 (2023).
  * Shields  _et al._ (2021) B. J. Shields, J. Stevens, J. Li, M. Parasram, F. Damani, J. I. M. Alvarado, J. M. Janey, R. P. Adams, and A. G. Doyle, “Bayesian reaction optimization as a tool for chemical synthesis,” Nature 590, 89–96 (2021).
  * Häse, Roch, and Aspuru-Guzik (2019) F. Häse, L. M. Roch, and A. Aspuru-Guzik, “Next-generation experimentation with self-driving laboratories,” Trends in Chemistry 1, 282–291 (2019).
  * Tabor  _et al._ (2018a) D. P. Tabor, L. M. Roch, S. K. Saikin, C. Kreisbeck, D. Sheberla, J. H. Montoya, S. Dwaraknath, M. Aykol, C. Ortiz, H. Tribukait, _et al._ , “Accelerating the discovery of materials for clean energy in the era of smart automation,” Nature Reviews Materials 3, 5–20 (2018a).
  * Narayanan  _et al._ (2021) H. Narayanan, F. Dingfelder, A. Butté, N. Lorenzen, M. Sokolov, and P. Arosio, “Machine learning for biologics: Opportunities for protein engineering, developability, and formulation,” Trends in Pharmacological Sciences 42, 151–165 (2021).
  * Seifrid  _et al._ (2022) M. Seifrid, J. Hattrick-Simpers, A. Aspuru-Guzik, T. Kalil, and S. Cranford, “Reaching critical mass: Crowdsourcing designs for the next generation of materials acceleration platforms,” Matter 5, 1972–1976 (2022).
  * Stach  _et al._ (2021) E. Stach, B. DeCost, A. G. Kusne, J. Hattrick-Simpers, K. A. Brown, K. G. Reyes, J. Schrier, S. Billinge, T. Buonassisi, I. Foster, C. P. Gomes, J. M. Gregoire, A. Mehta, J. Montoya, E. Olivetti, C. Park, E. Rotenberg, S. K. Saikin, S. Smullin, V. Stanev, and B. Maruyama, “Autonomous experimentation systems for materials development: A community perspective,” Matter 4, 2702–2726 (2021).
  * Alán, Persson, and Tribukait-Vasconcelos (2018) A.-G. Alán, K. Persson, and H. Tribukait-Vasconcelos, “Materials Acceleration Platform: Accelerating advanced energy materials discovery by integrating high-throughput methods with artificial intelligence,” Tech. Rep. (Mission Innovation, 2018) https://escholarship.org/uc/item/3926s13w.
  * Kitano (2021) H. Kitano, “Nobel Turing challenge: Creating the engine for scientific discovery,” npj Systems Biology and Applications 7, 29 (2021).
  * Shapere (1964) D. Shapere, “The structure of scientific revolutions,” The Philosophical Review 73, 383–394 (1964).
  * Burger  _et al._ (2020) B. Burger, P. M. Maffettone, V. V. Gusev, C. M. Aitchison, Y. Bai, X. Wang, X. Li, B. M. Alston, B. Li, R. Clowes, N. Rankin, B. Harris, R. S. Sprick, and A. I. Cooper, “A mobile robotic chemist,” Nature 583, 237–241 (2020).
  * Maffettone  _et al._ (2022a) P. M. Maffettone, S. Campbell, M. D. Hanwell, S. Wilkins, and D. Olds, “Delivering real-time multi-modal materials analysis with enterprise beamlines,” Cell Reports Physical Science 3 (2022a).
  * Wilkinson  _et al._ (2016) M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, _et al._ , “The FAIR guiding principles for scientific data management and stewardship,” Scientific Data 3, 160018 (2016).
  * Brinson  _et al._ (2022) L. Brinson, L. Bartolo, B. Blaiszik, D. Elbert, I. Foster, A. Strachan, and P. Voorhees, “FAIR data will fuel a revolution in materials research,” arXiv preprint arXiv:2204.02881 (2022).
  * Dempsey  _et al._ (2022) W. Dempsey, I. Foster, S. Fraser, and C. Kesselman, “Sharing begins at home: How continuous and ubiquitous FAIRness can enhance research productivity and data reuse,” Harvard Data Science Review 4 (2022).
  * Pendleton  _et al._ (2019) I. M. Pendleton, G. Cattabriga, Z. Li, M. A. Najeeb, S. A. Friedler, A. J. Norquist, E. M. Chan, and J. Schrier, “Experiment specification, capture and laboratory automation technology (escalate): a software pipeline for automated chemical experimentation and data management,” MRS Communications 9, 846–859 (2019).
  * Blaiszik  _et al._ (2019a) B. Blaiszik, L. Ward, M. Schwarting, J. Gaff, R. Chard, D. Pike, K. Chard, and I. Foster, “A data ecosystem to support machine learning in materials science,” MRS Communications 9, 1125–1133 (2019a).
  * Allan  _et al._ (2019) D. Allan, T. Caswell, S. Campbell, and M. Rakitin, “Bluesky’s ahead: A multi-facility collaboration for an a la carte software project for data acquisition and management,” Synchrotron Radiation News 32, 19–22 (2019).
  * Mahnke, Leitner, and Damm (2009) W. Mahnke, S.-H. Leitner, and M. Damm,  _OPC Unified Architecture_ , 2009th ed. (Springer, Berlin, Germany, 2009).
  * Bär, Hochstrasser, and Papenfuß (2012) H. Bär, R. Hochstrasser, and B. Papenfuß, “Sila: Basic standards for rapid integration in laboratory automation,” Journal of Laboratory Automation 17, 86–95 (2012).
  * Vescovi  _et al._ (2022) R. Vescovi, R. Chard, N. D. Saint, B. Blaiszik, J. Pruyne, T. Bicer, A. Lavens, Z. Liu, M. E. Papka, S. Narayanan, N. Schwarz, K. Chard, and I. Foster, “Linking scientific instruments and computation: Patterns, technologies, and experiences,” Patterns 3, 100606 (2022).
  * Pelkie and Pozzo (2023) B. G. Pelkie and L. D. Pozzo, “The laboratory of babel: highlighting community needs for integrated materials data management,” Digital Discovery , – (2023).
  * Berman  _et al._ (2000) H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne, “The Protein Data Bank,” Nucleic Acids Res. 28, 235–242 (2000).
  * Hall  _et al._ (2005) S. R. Hall, J. D. Westbrook, N. Spadaccini, I. D. Brown, H. J. Bernstein, and B. McMahon, “Specification of the crystallographic information file (cif),” (Springer Netherlands, Dordrecht, 2005) pp. 20–36.
  * Breck  _et al._ (2019) E. Breck, M. Zinkevich, N. Polyzotis, S. Whang, and S. Roy, “Data validation for machine learning,” in  _Proceedings of SysML_ (2019).
  * Tremouilhac  _et al._ (2020) P. Tremouilhac, C.-L. Lin, P.-C. Huang, Y.-C. Huang, A. Nguyen, N. Jung, F. Bach, R. Ulrich, B. Neumair, A. Streit, _et al._ , “The repository chemotion: infrastructure for sustainable research in chemistry,” Angewandte Chemie International Edition 59, 22771–22778 (2020).
  * Jain  _et al._ (2013) A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, _et al._ , “Commentary: The materials project: A materials genome approach to accelerating materials innovation,” APL materials 1, 011002 (2013).
  * Taylor  _et al._ (2014) R. H. Taylor, F. Rose, C. Toher, O. Levy, K. Yang, M. B. Nardelli, and S. Curtarolo, “A restful api for exchanging materials data in the aflowlib. org consortium,” Computational materials science 93, 178–192 (2014).
  * Blaiszik  _et al._ (2016) B. Blaiszik, K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, and I. Foster, “The materials data facility: data services to advance materials science research,” Jom 68, 2045–2052 (2016).
  * Blaiszik  _et al._ (2019b) B. Blaiszik, L. Ward, M. Schwarting, J. Gaff, R. Chard, D. Pike, K. Chard, and I. Foster, “A data ecosystem to support machine learning in materials science,” MRS Communications 9, 1125–1133 (2019b).
  * Saal  _et al._ (2013) J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton, “Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd),” Jom 65, 1501–1509 (2013).
  * Draxl and Scheffler (2019) C. Draxl and M. Scheffler, “The nomad laboratory: from data sharing to artificial intelligence,” Journal of Physics: Materials 2, 036001 (2019).
  * Herring  _et al._ (2020) P. Herring, C. B. Gopal, M. Aykol, J. H. Montoya, A. Anapolsky, P. M. Attia, W. Gent, J. S. Hummelshøj, L. Hung, H.-K. Kwon, _et al._ , “BEEP: A Python library for battery evaluation and early prediction,” SoftwareX 11, 100506 (2020).
  * Apted and Murphy (2021) M. Apted and S. Murphy, “Choosing a ci/cd approach: Aws services with bighat biosciences,”  (2021).
  * Chard  _et al._ (2023) R. Chard, J. Pruyne, K. McKee, J. Bryan, B. Raumann, R. Ananthakrishnan, K. Chard, and I. T. Foster, “Globus automation services: Research process automation across the space–time continuum,” Future Generation Computer Systems (2023).
  * Maffettone  _et al._ (2022b) P. Maffettone, D. Allan, S. I. Campbell, M. R. Carbone, T. Caswell, B. L. DeCost, D. Gavrilov, M. Hanwell, H. Joress, J. Lynch, B. Ravel, S. Wilkins, J. Wlodek, and D. Olds, “Self-driving multimodal studies at user facilities,” in  _AI for Accelerated Materials Design NeurIPS 2022 Workshop_ (2022).
  * Raccuglia  _et al._ (2016) P. Raccuglia, K. C. Elbert, P. D. F. Adler, C. Falk, M. B. Wenny, A. Mollo, M. Zeller, S. A. Friedler, J. Schrier, and A. J. Norquist, “Machine-learning-assisted materials discovery using failed experiments,” Nature 533, 73–76 (2016).
  * Harris  _et al._ (2020) C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature 585, 357–362 (2020).
  * Hohlbein  _et al._ (2022) J. Hohlbein, B. Diederich, B. Marsikova, E. G. Reynaud, S. Holden, W. Jahr, R. Haase, and K. Prakash, “Open microscopy in the life sciences: quo vadis?” Nature Methods 19, 1020–1025 (2022).
  * Tabor  _et al._ (2018b) D. P. Tabor, L. M. Roch, S. K. Saikin, C. Kreisbeck, D. Sheberla, J. H. Montoya, S. Dwaraknath, M. Aykol, C. Ortiz, H. Tribukait, C. Amador-Bedolla, C. J. Brabec, B. Maruyama, K. A. Persson, and A. Aspuru-Guzik, “Accelerating the discovery of materials for clean energy in the era of smart automation,” Nature Reviews Materials 3, 5–20 (2018b).
  * Vasquez (2019) J. Vasquez, “Jubilee: A toolchanging homage to 3d printer hackers everywhere,”  (2019).
  * King  _et al._ (2009) R. D. King, J. Rowland, S. G. Oliver, M. Young, W. Aubrey, E. Byrne, M. Liakata, M. Markham, P. Pir, L. N. Soldatova, A. Sparkes, K. E. Whelan, and A. Clare, “The automation of science,” Science 324, 85–89 (2009).
  * MacLeod  _et al._ (2020) B. P. MacLeod, F. G. Parlane, T. D. Morrissey, F. Häse, L. M. Roch, K. E. Dettelbach, R. Moreira, L. P. Yunker, M. B. Rooney, J. R. Deeth, _et al._ , “Self-driving laboratory for accelerated discovery of thin-film materials,” Science Advances 6, eaaz8867 (2020).
  * Fakhruldeen  _et al._ (2022) H. Fakhruldeen, G. Pizzuto, J. Glawucki, and A. I. Cooper, “Archemist: Autonomous robotic chemistry system architecture,” IEEE International Conference on Robotics and Automation (2022).
  * Quigley  _et al._ (2009) M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng, “ROS: An open-source Robot Operating System,” in  _ICRA Workshop on Open Source Software_ , Vol. 3 (Kobe, Japan, 2009) p. 5.
  * Dalesio, Kozubal, and Kraimer (1991) L. R. Dalesio, A. J. Kozubal, and M. R. Kraimer, “Epics architecture,” in  _International conference on accelerator and large experimental physics control systems_ (1991).
  * Pinter and Yoaz (1996) S. Pinter and A. Yoaz, “Tango: a hardware-based data prefetching technique for superscalar processors,” in  _Proceedings of the 29th Annual IEEE/ACM International Symposium on Microarchitecture. MICRO 29_ (1996) pp. 214–225.
  * MacLeod  _et al._ (2022a) B. P. MacLeod, F. G. L. Parlane, A. K. Brown, J. E. Hein, and C. P. Berlinguette, “Flexible automation accelerates materials discovery,” Nature Materials 21, 722–726 (2022a).
  * Pizzuto  _et al._ (2022) G. Pizzuto, J. De Berardinis, L. Longley, H. Fakhruldeen, and A. I. Cooper, “Solis: Autonomous solubility screening using deep neural networks,” in  _2022 International Joint Conference on Neural Networks (IJCNN)_ (2022) pp. 1–7.
  * Lim  _et al._ (2021) J. X.-Y. Lim, D. Leow, Q.-C. Pham, and C.-H. Tan, “Development of a robotic system for automatic organic chemistry synthesis,” IEEE Transactions on Automation Science and Engineering 18, 2185–2190 (2021).
  * Xu  _et al._ (2021) H. Xu, Y. R. Wang, S. Eppel, A. Aspuru-Guzik, F. Shkurti, and A. Garg, “Seeing glass: Joint point cloud and depth completion for transparent objects,” (2021), arXiv:2110.00087 [cs.CV] .
  * Wang  _et al._ (2023) Y. R. Wang, Y. Zhao, H. Xu, S. Eppel, A. Aspuru-Guzik, F. Shkurti, and A. Garg, “Mvtrans: Multi-view perception of transparent objects,” (2023), arXiv:2302.11683 [cs.RO] .
  * Zwirnmann  _et al._ (2023) H. Zwirnmann, D. Knobbe, U. Culha, and S. Haddadin, “Dual-material 3d-printed pacome-like fingers for flexible biolaboratory automation,”  (2023).
  * Yoshikawa  _et al._ (2023) N. Yoshikawa, K. Darvish, A. Garg, and A. Aspuru-Guzik, “Digital pipette: Open hardware for liquid transfer in self-driving laboratories.” (2023).
  * Steiner  _et al._ (2019) S. Steiner, J. Wolf, S. Glatzel, A. Andreou, J. M. Granda, G. Keenan, T. Hinkley, G. Aragon-Camarasa, P. J. Kitson, D. Angelone, and L. Cronin, “Organic synthesis in a modular robotic system driven by a chemical programming language,” Science 363, eaav2211 (2019), https://www.science.org/doi/pdf/10.1126/science.aav2211 .
  * Manzano  _et al._ (2022) J. S. Manzano, W. Hou, S. S. Zalesskiy, P. Frei, H. Wang, P. J. Kitson, and L. Cronin, “An autonomous portable platform for universal chemical synthesis,” Nature Chemistry 14, 1311–1318 (2022).
  * Soldatov  _et al._ (2021) M. A. Soldatov, V. V. Butova, D. Pashkov, M. A. Butakova, P. V. Medvedev, A. V. Chernov, and A. V. Soldatov, “Self-driving laboratories for development of new functional materials and optimizing known reactions,” Nanomaterials 11 (2021).
  * Hertzberg and Pope (2000) R. P. Hertzberg and A. J. Pope, “High-throughput screening: new technology for the 21st century,” Current opinion in chemical biology 4, 445–451 (2000).
  * Battersby and Trau (2002) B. J. Battersby and M. Trau, “Novel miniaturized systems in high-throughput screening,” Trends in biotechnology 20, 167–173 (2002).
  * Potyrailo and Amis (2012) R. A. Potyrailo and E. J. Amis,  _High-throughput analysis: a tool for combinatorial materials science_ (Springer Science & Business Media, 2012).
  * Karthik, Kasiviswanathan, and Raj (2016) V. Karthik, K. Kasiviswanathan, and B. Raj,  _Miniaturized testing of engineering materials_ (CRC Press, 2016).
  * Buitrago Santanilla  _et al._ (2015) A. Buitrago Santanilla, E. L. Regalado, T. Pereira, M. Shevlin, K. Bateman, L.-C. Campeau, J. Schneeweis, S. Berritt, Z.-C. Shi, P. Nantermet, _et al._ , “Nanomole-scale high-throughput chemistry for the synthesis of complex molecules,” Science 347, 49–53 (2015).
  * Epps  _et al._ (2020) R. W. Epps, M. S. Bowen, A. A. Volk, K. Abdel-Latif, S. Han, K. G. Reyes, A. Amassian, and M. Abolhasani, “Artificial chemist: An autonomous quantum dot synthesis bot,” Advanced Materials 32, 2001626 (2020).
  * Delgado-Licona and Abolhasani (2023) F. Delgado-Licona and M. Abolhasani, “Research acceleration in self-driving labs: Technological roadmap toward accelerated materials and molecular discovery,” Advanced Intelligent Systems n/a, 2200331 (2023).
  * Volk  _et al._ (2022) A. A. Volk, Z. S. Campbell, M. Y. Ibrahim, J. A. Bennett, and M. Abolhasani, “Flow chemistry: A sustainable voyage through the chemical universe en route to smart manufacturing,” Annual Review of Chemical and Biomolecular Engineering 13, 45–72 (2022), pMID: 35259931.
  * Takeuchi, Lauterbach, and Fasolka (2005) I. Takeuchi, J. Lauterbach, and M. J. Fasolka, “Combinatorial materials synthesis,” Materials Today 8, 18–26 (2005).
  * Ludwig (2019) A. Ludwig, “Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods,” NPJ Computational Materials 5, 70 (2019).
  * Nikolaev  _et al._ (2014) P. Nikolaev, D. Hooper, N. Perea-Lopez, M. Terrones, and B. Maruyama, “Discovery of wall-selective carbon nanotube growth conditions via automated experimentation,” ACS Nano 8, 10214–10222 (2014).
  * Feng, Ueda, and Levkin (2018) W. Feng, E. Ueda, and P. A. Levkin, “Droplet microarrays: From surface patterning to high-throughput applications,” Advanced materials 30, 1706111 (2018).
  * Rosenfeld  _et al._ (2020) A. Rosenfeld, C. Oelschlaeger, R. Thelen, S. Heissler, and P. A. Levkin, “Miniaturized high-throughput synthesis and screening of responsive hydrogels using nanoliter compartments,” Materials Today Bio 6, 100053 (2020).
  * Seifermann  _et al._ (2023) M. Seifermann, P. Reiser, P. Friederich, and P. Levkin, “High-throughput synthesis and machine learning assisted design of photodegradable hydrogels,” (2023).
  * Saygin  _et al._ (2021) V. Saygin, B. Xu, S. B. Andersson, and K. A. Brown, “Closed-loop nanopatterning of liquids with dip-pen nanolithography,” ACS Applied Materials & Interfaces 13, 14710–14717 (2021).
  * Brown (2022) K. A. Brown, “Scanning probes as a materials automation platform with extremely miniaturized samples,” Matter 5, 3112–3123 (2022).
  * Zech, Claus, and Hönicke (2002) T. Zech, P. Claus, and D. Hönicke, “Miniaturized reactors in combinatorial catalysis and high-throughput experimentation,” Chimia 56, 611–611 (2002).
  * Lohrengel, Moehring, and Pilaski (2000) M. Lohrengel, A. Moehring, and M. Pilaski, “Electrochemical surface analysis with the scanning droplet cell,” Fresenius’ journal of analytical chemistry 367, 334–339 (2000).
  * Jenewein  _et al._ (2022) K. J. Jenewein, G. D. Akkoc, A. Kormányos, and S. Cherevko, “Automated high-throughput activity and stability screening of electrocatalysts,” Chem Catalysis 2, 2778–2794 (2022).
  * Zhang  _et al._ (2020) W. Zhang, R. Wang, F. Luo, P. Wang, and Z. Lin, “Miniaturized electrochemical sensors and their point-of-care applications,” Chinese Chemical Letters 31, 589–600 (2020).
  * Roch  _et al._ (2020) L. M. Roch, F. Häse, C. Kreisbeck, T. Tamayo-Mendoza, L. P. Yunker, J. E. Hein, and A. Aspuru-Guzik, “ChemOS: An orchestration software to democratize autonomous discovery,” PLoS One 15, e0229862 (2020).
  * Salley  _et al._ (2020) D. Salley, G. Keenan, J. Grizou, A. Sharma, S. Martín, and L. Cronin, “A nanomaterials discovery robot for the darwinian evolution of shape programmable gold nanoparticles,” Nature communications 11, 2771 (2020).
  * Rajak  _et al._ (2021) P. Rajak, A. Krishnamoorthy, A. Mishra, R. Kalia, A. Nakano, and P. Vashishta, “Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials,” npj Computational Materials 7, 108 (2021).
  * Li  _et al._ (2020) J. Li, J. Li, R. Liu, Y. Tu, Y. Li, J. Cheng, T. He, and X. Zhu, “Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab,” Nature communications 11, 2046 (2020).
  * Maffettone  _et al._ (2021) P. M. Maffettone, J. K. Lynch, T. A. Caswell, C. E. Cook, S. I. Campbell, and D. Olds, “Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities,” Machine Learning: Science and Technology 2, 025025 (2021).
  * Gongora  _et al._ (2020) A. E. Gongora, B. Xu, W. Perry, C. Okoye, P. Riley, K. G. Reyes, E. F. Morgan, and K. A. Brown, “A Bayesian experimental autonomous researcher for mechanical design,” Science advances 6, eaaz1708 (2020).
  * MacLeod  _et al._ (2022b) B. P. MacLeod, F. G. Parlane, C. C. Rupnow, K. E. Dettelbach, M. S. Elliott, T. D. Morrissey, T. H. Haley, O. Proskurin, M. B. Rooney, N. Taherimakhsousi, _et al._ , “A self-driving laboratory advances the Pareto front for material properties,” Nature communications 13, 995 (2022b).
  * Häse, Roch, and Aspuru-Guzik (2018) F. Häse, L. M. Roch, and A. Aspuru-Guzik, “Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories,” Chemical science 9, 7642–7655 (2018).
  * Daulton, Balandat, and Bakshy (2021) S. Daulton, M. Balandat, and E. Bakshy, “Parallel Bayesian optimization of multiple noisy objectives with expected hypervolume improvement,” Advances in Neural Information Processing Systems 34, 2187–2200 (2021).
  * Torres  _et al._ (2022) J. A. G. Torres, S. H. Lau, P. Anchuri, J. M. Stevens, J. E. Tabora, J. Li, A. Borovika, R. P. Adams, and A. G. Doyle, “A multi-objective active learning platform and web app for reaction optimization,” Journal of the American Chemical Society 144, 19999–20007 (2022).
  * Balandat  _et al._ (2020) M. Balandat, B. Karrer, D. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy, “BoTorch: A framework for efficient Monte-Carlo Bayesian optimization,” Advances in neural information processing systems 33, 21524–21538 (2020).
  * Liang  _et al._ (2021) Q. Liang, A. E. Gongora, Z. Ren, A. Tiihonen, Z. Liu, S. Sun, J. R. Deneault, D. Bash, F. Mekki-Berrada, S. A. Khan, _et al._ , “Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains,” npj Computational Materials 7, 188 (2021).
  * Epps  _et al._ (2021) R. W. Epps, A. A. Volk, M. Y. Ibrahim, and M. Abolhasani, “Universal self-driving laboratory for accelerated discovery of materials and molecules,” Chem 7, 2541–2545 (2021).
  * Sanchez-Lengeling and Aspuru-Guzik (2018) B. Sanchez-Lengeling and A. Aspuru-Guzik, “Inverse molecular design using machine learning: Generative models for matter engineering,” Science 361, 360–365 (2018).
  * Gómez-Bombarelli  _et al._ (2018) R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik, “Automatic chemical design using a data-driven continuous representation of molecules,” ACS central science 4, 268–276 (2018).
  * Noh  _et al._ (2020) J. Noh, G. H. Gu, S. Kim, and Y. Jung, “Machine-enabled inverse design of inorganic solid materials: promises and challenges,” Chemical Science 11, 4871–4881 (2020).
  * Gao and Coley (2020) W. Gao and C. W. Coley, “The synthesizability of molecules proposed by generative models,” Journal of chemical information and modeling 60, 5714–5723 (2020).
  * Bilodeau  _et al._ (2022) C. Bilodeau, W. Jin, T. Jaakkola, R. Barzilay, and K. F. Jensen, “Generative models for molecular discovery: Recent advances and challenges,” Wiley Interdisciplinary Reviews: Computational Molecular Science 12, e1608 (2022).
  * Gao, Raghavan, and Coley (2022) W. Gao, P. Raghavan, and C. W. Coley, “Autonomous platforms for data-driven organic synthesis,” Nature Communications 13, 1075 (2022).
  * Bradshaw  _et al._ (2019) J. Bradshaw, B. Paige, M. J. Kusner, M. Segler, and J. M. Hernández-Lobato, “A model to search for synthesizable molecules,” in  _Advances in Neural Information Processing Systems_, Vol. 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019).
  * Kearnes  _et al._ (2021) S. M. Kearnes, M. R. Maser, M. Wleklinski, A. Kast, A. G. Doyle, S. D. Dreher, J. M. Hawkins, K. F. Jensen, and C. W. Coley, “The open reaction database,” Journal of the American Chemical Society 143, 18820–18826 (2021).
  * Luo  _et al._ (2022) Y. Luo, S. Bag, O. Zaremba, A. Cierpka, J. Andreo, S. Wuttke, P. Friederich, and M. Tsotsalas, “Mof synthesis prediction enabled by automatic data mining and machine learning,” Angewandte Chemie International Edition 61, e202200242 (2022).
  * Gupta, Zaki, and Krishnan (2022) T. Gupta, M. Zaki, and N. A. Krishnan, “Matscibert: A materials domain language model for text mining and information extraction,” npj Computational Materials 8, 102 (2022).
  * Dunn  _et al._ (2022) A. Dunn, J. Dagdelen, N. Walker, S. Lee, A. S. Rosen, G. Ceder, K. Persson, and A. Jain, “Structured information extraction from complex scientific text with fine-tuned large language models,” arXiv preprint arXiv:2212.05238 (2022).
  * Krenn  _et al._ (2022) M. Krenn, R. Pollice, S. Y. Guo, M. Aldeghi, A. Cervera-Lierta, P. Friederich, G. dos Passos Gomes, F. Häse, A. Jinich, A. Nigam, _et al._ , “On scientific understanding with artificial intelligence,” Nature Reviews Physics 4, 761–769 (2022).
  * Friederich  _et al._ (2021) P. Friederich, M. Krenn, I. Tamblyn, and A. Aspuru-Guzik, “Scientific intuition inspired by machine learning-generated hypotheses,” Machine Learning: Science and Technology 2, 025027 (2021).
  * Khoury, Baliban, and Floudas (2011) G. A. Khoury, R. C. Baliban, and C. A. Floudas, “Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database,” Sci. Rep. 1, 90 (2011).
  * Ying  _et al._ (2019) Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “GNNExplainer: Generating explanations for graph neural networks,” Advances in neural information processing systems 32 (2019).
  * Teufel  _et al._ (2022) J. Teufel, L. Torresi, P. Reiser, and P. Friederich, “Megan: Multi-explanation graph attention network,” arXiv preprint arXiv:2211.13236 (2022).
  * Oviedo  _et al._ (2022) F. Oviedo, J. L. Ferres, T. Buonassisi, and K. T. Butler, “Interpretable and explainable machine learning for materials science and chemistry,” Accounts of Materials Research 3, 597–607 (2022).
  * Zhong  _et al._ (2022) X. Zhong, B. Gallagher, S. Liu, B. Kailkhura, A. Hiszpanski, and T. Y.-J. Han, “Explainable machine learning in materials science,” npj Computational Materials 8, 204 (2022).
  * Pilania (2021) G. Pilania, “Machine learning in materials science: From explainable predictions to autonomous design,” Computational Materials Science 193, 110360 (2021).
  * Kailkhura  _et al._ (2019) B. Kailkhura, B. Gallagher, S. Kim, A. Hiszpanski, and T. Y.-J. Han, “Reliable and explainable machine-learning methods for accelerated material discovery,” npj Computational Materials 5, 108 (2019).
  * Wellawatte, Seshadri, and White (2022) G. P. Wellawatte, A. Seshadri, and A. D. White, “Model agnostic generation of counterfactual explanations for molecules,” Chemical science 13, 3697–3705 (2022).
  * Cheng  _et al._ (2018) Y. Cheng, R. Matthiesen, S. Farooq, J. Johansen, H. Hu, and L. Ma, “The evolution of investment patterns on advanced manufacturing technology (amt) in manufacturing operations: A longitudinal analysis,” International Journal of Production Economics 203, 239–253 (2018).
  * Deming (1982) W. E. Deming,  _Quality, productivity, and competitive position_ (Massachusetts Inst Technology, 1982).
  * Ansar and Flyvbjerg (2022) A. Ansar and B. Flyvbjerg, “How to solve big problems: Bespoke versus platform strategies,” Oxford Review of Economic Policy 38, 338–368 (2022).
  * Lindsay and Morgan (2021) E. D. Lindsay and J. R. Morgan, “The CSU engineering model: educating student engineers through PBL, WPL and an online, on demand curriculum,” European Journal of Engineering Education 46, 637–661 (2021).
  * Sun, Brown, and Kusne (2022) S. Sun, K. Brown, and A. G. Kusne, “Teaching machine learning to materials scientists: Lessons from hosting tutorials and competitions,” Matter 5, 1620–1622 (2022).
  * Note (1) Https://pages.nist.gov/remi/.
  * Baird and Sparks (2022) S. G. Baird and T. D. Sparks, “What is a minimal working example for a self-driving laboratory?” Matter 5, 4170–4178 (2022).
  * Gamarra  _et al._ (2021) M. Gamarra, A. Dominguez, J. Velazquez, and H. Páez, “A gamification strategy in engineering education—a case study on motivation and engagement,” Computer Applications in Engineering Education 30, 472–482 (2021).
  * Urbina  _et al._ (2022) F. Urbina, F. Lentzos, C. Invernizzi, and S. Ekins, “Dual use of artificial-intelligence-powered drug discovery,” Nature Machine Intelligence 4, 189–191 (2022).
  * Hickman  _et al._ (2022) R. J. Hickman, P. Bannigan, Z. Bao, A. Aspuru-Guzik, and C. Allen, “Self-driving laboratories: A paradigm shift in nanomedicine development,” Matter (2022).
  * Turchin and Denkenberger (2020) A. Turchin and D. Denkenberger, “Classification of global catastrophic risks connected with artificial intelligence,” AI & Society (2020), 10.1007/s00146-018-0845-5.
  * O’Brien and Nelson (2020) J. O’Brien and C. Nelson, “Assessing the risks posed by the convergence of artificial intelligence and biotechnology,” Health Security (2020), 10.1089/hs.2019.0122.
  * Koehler and Hilton (2022) A. Koehler and B. Hilton, “Preventing catastrophic pandemics,” 80000hours.org (2022).
  * Hilton (2022) B. Hilton, “Preventing an ai-related catastrophe,” 80000hours.org (2022).
  * Pyzer-Knapp  _et al._ (2022) E. O. Pyzer-Knapp, J. W. Pitera, P. W. J. Staar, S. Takeda, T. Laino, D. P. Sanders, J. Sexton, J. R. Smith, and A. Curioni, “Accelerating materials discovery using artificial intelligence, high performance computing and robotics,” npj Computational Materials 8, 84 (2022).
  * Giro  _et al._ (2023) R. Giro, M. Elkaref, H. Hsu, N. Herr, G. De Mel, and M. Steiner, “Hsiang-han hsu automatic, physical data extraction from scientific publications for application to generative molecular design in computational materials discovery,” in  _March Meeting_ (American Physical Society, 2023).