# The Optical Design Concept for the Atacama Large Aperture Submillimeter
Telescope (AtLAST)

Patricio A. Gallardo  Kavli Institute for Cosmological Physics, The University
of Chicago, USA  Roberto Puddu  Instituto de Astrofísica and Centro de Astro-
Ingeniería, Facultad de Física, Pontificia Universidad Católica de Chile,
Santiago, Chile  Tony Mroczkowski  European Southern Observatory, Karl-
Schwarzschild-Str. 2, Garching 85748, Germany  Martin Timpe  OHB Digital
Connect, Weberstraße 21, D-55130 Mainz, Germany  Pierre Dubois-dit-Bonclaude
OHB Digital Connect, Weberstraße 21, D-55130 Mainz, Germany  Manuel Groh  OHB
Digital Connect, Weberstraße 21, D-55130 Mainz, Germany  Matthias Reichert
OHB Digital Connect, Weberstraße 21, D-55130 Mainz, Germany  Claudia Cicone
Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029,
Blindern 0315, Oslo, Norway  Hans J. Kaercher  Independent Consultant,
Kirchgasse 4, D-61184 Karben, Germany

###### Abstract

The Atacama Large Aperture Submillimeter Telescope (AtLAST) aims to be the
premier next generation large diameter (50-meter) single dish observatory
capable of observations across the millimeter/sub-millimeter spectrum, from 30
to 950 GHz. The large primary mirror diameter, the 2-degree field of view and
its large 4.7-meter focal surface give AtLAST a high throughput (aperture size
times field of view) and grasp (throughput times spectral reach), with the
ability to illuminate $>\mathcal{O}(10^{7})$ detectors. The optical design
concept for AtLAST consists of a numerically optimized two-mirror Ritchey-
Chrétien system with an additional flat folding mirror, which enables a quick
selection among its planned six instrument positions. We present the optical
design concept and discuss the expected optical performance of AtLAST. We then
present design concepts that can be implemented in the receiver and instrument
optics to correct for astigmatism and mitigate the high degree of curvature of
the focal surface in order to recover significant fractions of the geometric
field of view at sub-millimeter wavelengths.

###### keywords:

Millimeter/Sub-Millimeter Wave, Telescope Design, Optics.

##  1 Introduction

The Atacama Large Aperture Submillimeter Telescope, AtLAST, is a proposed
fifty-meter telescope, capable of illuminating a 4.7-meter focal surface with
a two-degree field of view. AtLAST aims to be the premier wide-field mapping
telescope at frequencies spanning 30-950 GHz (1 cm - 350 $\mu$m), capable of
diffraction-limited observations with resulting resolutions of a few to a few
tens of arcseconds across the millimeter/sub-millimeter bands. The large
aperture, large field of view and large spectral grasp will give AtLAST the
ability to observe the microwave sky with unparalleled mapping speeds and
resolution.

The scientific objectives of AtLAST are broad and range from resolved
observations of the Sun, solar system objects, molecular clouds and star
forming regions, nearby galaxies, and large scale cosmic structures, as well
as detections of protoplanetary discs in our Galaxy and distant dusty star
forming galaxies out to their redshift of formation.[1, 2, 3, 4, 5, 6, 7, 8,
9]111The AtLAST science cases can be found in the Open Research Europe
collection, located here: https://open-research-
europe.ec.europa.eu/collections/atlast. A broader description of the AtLAST
design has been presented separately in Mroczkowski et al. 2024 [10], while
the scientific motivations are presented in Booth et al. 2024 (submitted to
these proceedings)[11]. These proceedings also include additional subsystem
descriptions for the telescope structure (Reichert et al. submitted)[12] and
an energy recovery system (Kiselev et al. submitted)[13], while Puddu et al.
(submitted) presents results of full-wave physical optics simulations for the
optical design discussed here.

This document describes the optical design parameters of the fifty-meter
AtLAST, including aperture definitions and image quality metrics. A basic
instrument consisting of three silicon lenses with biconic lens corrections is
presented to demonstrate a suitable instrument concept that can serve as a
starting point of future receiver development. Section 2 presents the
telescope optical design parameters. Section 3 presents the optical evaluation
of this design. In Section 4 we present a suitable concept for a three lens
camera instrument with biconic lenses. Finally, we conclude in Section 5.

Figure 1: Left: Optical layout of the Atacama Large Aperture Submillimeter
Telescope, AtLAST. Rays shown from the sky (top) reflect on the 50-meter
primary mirror (M1), reflect once more on a 12-meter secondary mirror (M2) to
reflect on an 8.7 tertiary mirror (M3), to finally focus on a 4.7 meter focal
surface (FS). Fields shown make an angle of $\pm 1$ degree from the boresight
(2-degree full field of view). Model includes the secondary aperture
obstruction. Right: Cross-section of AtLAST pointing towards the zenith to
replicate the orientation shown in the ray trace (left).

##  2 Optical Design Parameters

The telescope is composed of two curved surfaces in a Ritchey-Chrétien
configuration and a flat folding mirror that enables selection among the
multiple planned instrument locations [14]. The 50-meter diameter primary (M1)
illuminates a 12-meter diameter secondary (M2), which in turn illuminates a
flat $6.2\times 8.8\,$ meter fold mirror (M3). Light from the fold mirror
focuses into a curved focal surface (FS). The optical layout is shown in
Figure 1. Mirror shapes are given by a standard conic surface of revolution
$z(r)$ according to

$z(r)=\frac{r^{2}/R}{1+\sqrt{1-(1+k)(r/R)^{2}}},$ (1)

where $\rm R$ is the radius of curvature of the surface, and $\rm k$ is the
conic constant. The z-axis for the primary points in the direction from the
sky to the primary, while for the secondary it points from the secondary to
the tertiary and for the focal surface it points from the tertiary to the
focal surface. Numerical parameters for the evaluation of Equation 1, mirror
positions and apertures are given in Table 1.

| R | k | x | y | z | $R_{\rm min}$ | $R_{\rm max}$ | $R_{x}$ | $R_{y}$
---|---|---|---|---|---|---|---|---|---
Primary (M1) | -35000.00 | -1.01016 | 0.00 | 0.00 | 17500.00 | 5900 | 25000 | – | –
Secondary (M2) | -8331.68 | -1.76177 | 0.00 | 0.00 | 3624.25 | 980 | 6000 | – | –
Tertiary (M3) | $\infty$ | 0.00 | 0.00 | -383.35 | 25116.65 | – | – | 3050 | 4350
Focal Surface (FS) | 4243.85 | -0.79798 | 0.00 | 6000.00 | 25500.00 | – | 2350 | – | –
Table 1: Mirror shape parameters (R and k), locations (x, y, z), circular
active optical apertures and obscurations ($R_{\rm min}$, $R_{\rm max}$) and
elliptical M3 aperture $R_{x}$ $R_{y}$ for the AtLAST design. The radius of
curvature is denoted by $\rm R$, the conic constant is given by $k$. Positions
of surfaces in 3D space are given by x, y, z. Tertiary surface is defined by
an elliptical aperture, with semi-diameters given by $R_{x}$ and $R_{y}$.
Units for all columns are mm, except for the conic constant, which is unit-
less.

##  3 Telescope Optical Performance

In this section we detail figures of merit that describe the expected
performance of the telescope optical design such as diffraction-limited field
of view, spot diagrams, the Huygens point spread function, f-numbers and
mirror footprints.

a)

b)

c)

Figure 2: Image quality metrics for the telescope without re-imaging optics.
a) Diffraction-limited full field of view of the telescope as a function of
wavelength. The diffraction-limited performance at $350\,\rm\mu m$ covers 0.4
degrees on the sky, which corresponds to 0.9 meters in diameter at the
telescope focal surface. b) Simulated beam at the center field at a wavelength
of $\lambda=350\,\rm\mu m$. The double ring structure is produced by the
obstruction due to the secondary mirror, which produces oscillations that are
superposed to the ringing of the unobstructed optical system. This model
includes the effect of the obstruction of four spiderweb legs with a thickness
of $245\,\rm mm$, which cover the primary mirror up to a radius of 16.7
meters. c) Spot diagrams at wavelengths of $350\,\mu\rm m$, 2.0 mm and 10 mm
for the fields located at 0, 0.5 and 1.0 degrees from the boresight with
corresponding Strehl ratios of 0.99, 0.78 and 0.86. The black circle
represents the diffraction limit at these wavelengths, corresponding to the
first null in the Airy disk (${1.22\lambda}/{D}$). We note that the linear
dimension has been scaled with wavelength and is shown in the figure in
microns.

The full diffraction-limited field of view is quantified computing Strehl
ratios from $350\,\rm\mu m$ to $10\,\rm mm$. The diffraction-limited field of
view for the telescope is the maximum angle at which the Strehl ratio stays
above 0.8. Figure 2 (a) shows the full diffraction-limited field of view of
the telescope without re-imaging cameras as a function of wavelength. This
telescope-only field of view can be interpreted as a lower bound for the
achievable field of view as the reimaging optics can correct some aberrations
as discussed in Section 4. Figure 2 (b) shows a Huygens point spread function
computed using Ansys Zemax OpticStudio (222See
https://www.ansys.com/products/optics/ansys-zemax-opticstudio.) at
$\lambda=350\,\mu\rm m$. The concentric rings are an indication that the
system reaches the diffraction limit at this wavelength. The double ring
structure, with alternating thick and thin rings, is produced by the
obstruction of the aperture created by the secondary mirror, and the non-
uniform width of the rings is produced by the fourfold symmetry of the
obstructing leg struts that support the secondary mirror. An accompanying
proceedings paper by Puddu et al. in this series shows more detailed physical
optics beam calculations. Figure 2 (c) shows the spot diagrams for three field
positions (0, 0.5, and 1.0 degrees from the boresight) and the Airy disk size
for three wavelengths (0.35, 2.0, and 10 mm).

Figure 3: Left: Distribution of f-numbers in the telescope focal plane
surface. Right: Distribution of f-number with its 95% interval values.

The f-number for the telescope system is quantified by the angle of arrival of
the marginal ray to the focal surface relative to the chief ray. Using this
convention, the f-number is $2.62\pm 0.02$ in $95\%$ of the focal plane.
Figure 3 shows the f-number distribution across the focal surface (left) and a
histogram over the focal surface area (right). This focal number is chosen to
enable compatibility with planned and under construction instruments, like the
instruments for the Simons Observatory, CCAT and CMB-S4. [15, 16, 17, 18]

Footprints for the three mirrors and focal surface are shown in Figure 4. The
primary mirror obstruction (given by the 12-meter diameter secondary) shown in
the leftmost panel defines the size of the active area in the secondary
(second panel). The circular converging cone of light coming from the
secondary is intersected by the tilted flat tertiary mirror, which causes the
illumination of the tertiary mirror to be elliptical, with bigger ellipses on
the section of the mirror that is the closest to the secondary mirror, while
the smallest ellipses are in the region of the mirror that is the furthest
from the secondary, as shown in the third panel. The rightmost panel of Figure
4 shows the focal surface footprints for fields at 1 degree from the
boresight, note that a 2-degree field of view illuminates 4.7 meters at the
focal surface.

Figure 4: Optically active footprints on telescope surfaces. From left to
right: i) Primary mirror and the central obstruction produced by the twelve-
meter secondary. ii) Secondary mirror footprint, showing the central non-
illuminated region produced by the projection of the secondary obstruction on
the primary mirror for a 2-degree field of view. iii) Tertiary flat mirror.
iv) Focal plane. Surface apertures are given by Table 1.

##  4 Camera Concept

Although the development of instruments for AtLAST will be studied in the
future in response to specific scientific cases and their requirements, this
section describes a basic instrument concept composed of an array of cameras,
which can be evolved further in order to populate the focal plane with
instruments. This concept is intended as a starting point for future
development, and will most certainly be optimized further in the future.

The large focal plane of AtLAST needs to be divided into smaller optics tubes
of tens of centimeters in diameter according to current technologies. These
optics tubes contain reimaging optics, filters and cryogenic detectors. To
demonstrate the ability of populating the focal surface with optics tubes like
these, we use a minimal three-lens camera. The three lenses in the system can
be made out of silicon, this concept is common in ground-based cosmic
microwave background experiments [19, 18]. The first two lenses form an image
of the primary mirror on an intermediate cold stop (also called a ‘Lyot’
stop), this optical configuration allows to set the aperture of the primary
mirror and block unwanted thermal radiation from room temperature
surroundings.

The camera concept chosen in this demonstration uses an array of $40$ and
$20\,\rm{cm}$ diameter cameras arranged in a hexagonal lattice pattern. The 40
$\,\rm cm$ camera size corresponds to one of the largest optical quality
silicon formats that is easily commercially available at present time, and is
chosen to maximize the area coverage with a minimum number of 85 tubes. We
note that smaller camera concepts can be beneficial to optimize the system for
image quality, as a smaller field lens allows less variation in wavefront
error, which allows the optimization to find better solutions. For this
reason, it is foreseeable that the highest frequency cameras of AtLAST could
be made of smaller optics (of perhaps 20 or 30 cm diameter) to increase the
image quality at the detector plane. Another possible extension of the three-
camera concept presented here, is the use of a fourth lens, which gives more
degrees of freedom to correct optical aberrations.

The curvature of the focal surface in AtLAST constrains the orientation of the
camera windows. In the concept presented here the orientation of the camera
was chosen to be perpendicular to the focal surface at the center of the
window. For simplicity, a coaxial design was chosen, where all lenses share
the same optical axis perpendicular to the window. In order to accommodate an
optical axis perpendicular to the window, a prism is used, which for this
concept is made of alumina, although it can be made out of silicon. This prism
could in principle be combined with the first lens in the optical chain, which
would allow for a more compact design with a higher throughput. The choice of
orthogonality of the camera optical axis can be relaxed in future iterations
of this design in order to relieve the optical power of the alumina prism. The
implementation of these options are left for future studies.

Figure 5: Baseline camera concept for AtLAST. This camera system is composed
of 85 cameras, with a diameter of 40 cm each. The windows follow the shape of
the focal surface to keep the camera on axis. A prism is used to redirect the
beam towards the axis of the camera. This camera concept is composed of three
lenses and a Lyot stop. The second lens in the system can follow a biconic
shape, which corrects for astigmatism. Optical path from primary mirror is
shown on the left panel, right panel shows a zoom-in view of the 85 cameras.

Figure 5 shows the 85 cameras in the camera concept, which makes use of a
biconic lens to correct for the dominant optical aberration in the two-mirror
telescope. The optimal location to correct for astigmatism in this camera
concept is the Lyot stop, however, making the second lens a biconic surface
makes the system simpler and attenuates the risk of possible manufacturing
defects, which have the potential to contaminate the beam shape. The biconic
surface of the second lens has a shape according to

$z(x,y)=\frac{x^{2}/R_{x}+y^{2}/R_{y}}{1+\sqrt{1-(1+k_{x})(x/R_{x})^{2}-(1+k_{y})(y/R_{y})^{2}}},$
(2)

where $R_{x}$ and $R_{y}$ are the radii of curvature, and $k_{x}$ and $k_{y}$
are the conic constants along the _x, y_ axes. The local system of coordinates
for this lens is rotated around the optical axis to find the best orientation
to cancel the astigmatism. The first and third lens are made of a rotationally
symmetric standard surface.

Figure 6: Image quality for an 85-camera concept (Fig. 5) populating the
2-degree field of view of AtLAST. Lenses are $40\,\rm cm$ in diameter, which
allows to populate the 2-degree focal plane with minimal gaps. Figure shows 85
hexagons which show the percentage of the detector focal plane with a Strehl
ratio higher than 0.8. Left column shows this metric for a radially symmetric
second lens in the camera system, while the right column shows the same metric
for a biconic second lens. Diffraction-limited performance can be achieved at
4 millimeters and at shorter wavelengths the corrector lens allows a
significant improvement of the usable field of view.

The array of 85 cameras presented here with a biconic lens is compared to a
similar system with a rotationally symmetric second lens in Figure 6 for three
wavelengths of interest (1.2 mm, 2.0 mm and 4 mm). The column of the left
shows the percentage of the focal plane with a Strehl ratio higher than 0.8
for a radially symmetric second lens, while the column of the right shows the
same metric for a three-lens system with a biconic second lens. The top row
shows that a rotationally symmetric camera would yield seven diffraction-
limited cameras at $1.2\,\rm mm$, however by including a biconic lens, a
second ring of 7 additional cameras can be used with diffraction-limited
performance, in addition, the optical quality degrades slowly towards the
edges, which is an indication that the optical quality can be improved further
with detailed optimization, or that cameras at these locations can still be
used with a somewhat degraded performance. The second row shows that at
$2\,\rm mm$ of wavelength, the system becomes diffraction-limited in more than
95% of the array in 38 cameras, except for the two outermost rings. Here it
can be seen that the second to last ring of cameras still has a coverage with
more than $80\,\%$ of the focal plane showing diffraction-limited performance.
The last row shows that at a wavelength $\geq 4\,\rm mm$, the full 2 degree
field of AtLAST can be used with diffraction-limited performance.

An additional system with smaller 20 cm diameter lenses is shown in Figures 7
and 8. The columns show the percentage of the camera focal plane with Strehl
ratios higher than 0.8 (left) and 0.7 (right). At $350\,\rm\mu m$ the central
seven hexagons (0.4 degrees) show diffraction-limited performance in more than
$77\%$ of the array, and the central 19 cameras (0.8 degrees) can be populated
if the Strehl ratios are relaxed to 0.7. At $450\,\rm\mu m$ this coverage
remains unchanged, with better Strehl ratios in the 19 cameras. At
$850\,\rm\mu m$, 1.2 degrees of field of view can be used, which increases to
1.83 degrees (4.2 meters) for the full field of view if the Strehl ratios are
relaxed to 0.7. At still longer wavelengths ($\lambda>1.2$ mm), instruments
can more easily fill the entire 2-degree field of view (see Figure 8) without
relaxing the Strehl ratio requirements.

Figure 7: Optical quality for 85 cameras made using $20\,\rm cm$ diameter
lenses. Figure shows this system at wavelengths of $350\,\rm\mu m$,
$450\,\rm\mu m$ and $850\,\rm\mu m$. Figure 8 shows performance for millimeter
wavelengths. Cameras were optimized individually. Asymmetry in this diagram is
the result of numerical noise, and can be improved by forcing the system to be
radially symmetric.

Figure 8: Optical quality for 85 cameras made using $20\,\rm cm$ diameter
lenses. Figure shows this system at wavelengths of $1.2\,\rm mm$ and
$2.0\,mm$. Cameras were optimized individually. Asymmetry in this diagram is
the result of numerical noise, and can be improved by forcing the system to be
radially symmetric.

##  5 Conclusions

AtLAST is proposed to be the premier facility for single-dish millimeter and
sub-millimeter astronomy. We presented the optical design of AtLAST, which
consists of a fifty-meter primary mirror and a twelve-meter secondary mirror
with a fold mirror, which allows users to select among six different focal
surfaces. An optical demonstration for an array of three-lens biconic cameras
is presented, which can be used as a starting point for the future development
of instruments to deliver the highest throughput large dish telescope spanning
the millimeter and sub-millimeter spectrum.

###### Acknowledgements.

This project has received funding from the European Union’s Horizon 2020
research and innovation program under grant agreement No. 951815
(AtLAST).333See https://cordis.europa.eu/project/id/951815. The design
presented here evolved from pioneering work by R. Hills on various alternative
optics designs [14]. PAG acknowledges support from KICP at U. Chicago.

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2019

# The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag
Recommendation Approach

Presentation of work originally published in the Proc. of the 26th Intl. Conf.
on WWW

Elisabeth Lex111Graz University of Technology, ISDS, Inffeldgasse 13/V, 8010
Graz, Austria<EMAIL_ADDRESS>Dominik Kowald222Know-Center, Social
Computing Group, Inffeldgasse 13/VI, 8010 Graz, Austria  dkowald@know-
center.at

###### Abstract

In our work [kowald2017www], we study temporal usage patterns of Twitter
hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive
architecture ACT-R [anderson2004integrated] to model how a person reuses her
own, individual hashtags as well as hashtags from her social network. The BLL
equation accounts for the time-dependent decay of item exposure in human
memory. According to BLL, the usefulness of a piece of information (e.g., a
hashtag) is defined by how frequently and how recently it was used in the
past, following a time-dependent decay that is best modeled with a power-law
distribution. We used the BLL equation in our previous work to recommend tags
in social bookmarking systems [Kowald2016a]. Here [kowald2017www], we adopt
the BLL equation to model temporal reuse patterns of individual (i.e., reusing
own hashtags) and social hashtags (i.e., reusing hashtags, which has been
previously used by a followee) and to build a cognitive-inspired hashtag
recommendation algorithm. We demonstrate the efficacy of our approach in two
empirical social networks crawled from Twitter, i.e., CompSci and Random (for
details about the datasets, see [kowald2017www]). Our results show that our
approach can outperform current state-of-the-art hashtag recommendation
approaches.

(a)  (b)  (c)  (d)

Figure 1: The effect of time on individual and social hashtag reuse (plots are
in log-log scale).

Temporal Effects of Hashtag Reuse. To determine the plausibility of our
approach, we study hashtag use in our two empirical Twitter datasets, i.e.,
CompSci and Random. For each hashtag assignment, we investigate whether the
hashtag has either been used by the same user before (“individual”), by some
of her followees (“social”), by both (“individual/social”), by anyone else in
the dataset (“network”) or by neither of them (“external”). We find that
depending on the dataset, individual or social hashtag reuse can explain
approximately two-third of hashtag assignments. Also, both reuse types follow
a time-dependent decay that follows a power-law distribution, as shown in
Figure 1. This motivates our idea to model both individual as well as social
hashtag reuse and to recommend hashtags for new tweets with the BLL equation.

Experiments and Results. We implement the BLL equation in two variants, where
the first one (i.e., BLLI,S) predicts the hashtags of a user solely based on
past hashtag usage, and the second one (i.e., BLLI,S,C) combines BLLI,S with a
content-based tweet analysis to also incorporate the text of the currently
proposed tweet of a user. We evaluate our approach using standard evaluation
protocols and metrics, and we find that our approach provides significantly
higher prediction accuracy and ranking estimates than current state-of-the-art
hashtag recommendation algorithms in both scenarios (for more details about
the baselines, refer to [kowald2017www]), as shown in Figure 2.

(a)  (b)  (c)  (d)

Figure 2: Precision / Recall plots of our evaluation scenarios for $k$ = 1 -
10 recommended hashtags.

Conclusion and Reproducibility. We find that temporal effects play an
important role in hashtag reuse on Twitter. We propose our cognitive-inspired
hashtag recommendation approaches BLLI,S and BLLI,S,C to account for such
effects. We compare both algorithms to state-of-the-art hashtag recommendation
algorithms and find that our cognitive-inspired approaches outperform these
algorithms in terms of prediction accuracy and ranking. With our work, we aim
to contribute to the rich line of research on improving the use of hashtags in
social networks. We also hope to spark future work to utilize models from
human memory theory to model and explain digital traces and user behavior
online. For the sake of reproducibility, we implement and evaluate our
approach by extending our open-source tag recommender benchmarking framework
TagRec. The source code and framework are freely accessible for scientific
purposes on the Web333https://github.com/learning-layers/TagRec.

Keywords: hashtag recommendation, ACT-R, temporal effects, hashtag reuse, user
behavior modeling


# Materials Cloud, a platform for open computational science

Leopold Talirz Corresponding authors: Leopold Talirz
(leopold.talirz@gmail.com), Giovanni Pizzi (giovanni.pizzi@epfl.ch), Nicola
Marzari (nicola.marzari@epfl.ch)These authors contributed equally. National
Centre for Computational Design and Discovery of Novel Materials (MARVEL),
École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
Theory and Simulation of Materials (THEOS), Faculté des Sciences et Techniques
de l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Laboratory of Molecular Simulation (LSMO), Institut des Sciences
et Ingenierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne,
CH-1951 Sion, Switzerland  Snehal Kumbhar† National Centre for Computational
Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale
de Lausanne, CH-1015 Lausanne, Switzerland  Theory and Simulation of Materials
(THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Elsa
Passaro† National Centre for Computational Design and Discovery of Novel
Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015
Lausanne, Switzerland  Theory and Simulation of Materials (THEOS), Faculté des
Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingenierie Chimiques, Valais, École
Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland  Aliaksandr V.
Yakutovich  National Centre for Computational Design and Discovery of Novel
Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015
Lausanne, Switzerland  Theory and Simulation of Materials (THEOS), Faculté des
Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingenierie Chimiques, Valais, École
Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland  Valeria Granata
National Centre for Computational Design and Discovery of Novel Materials
(MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Theory and Simulation of Materials (THEOS), Faculté des Sciences
et Techniques de l’Ingénieur, École Polytechnique Fédérale de Lausanne,
CH-1015 Lausanne, Switzerland  Fernando Gargiulo  National Centre for
Computational Design and Discovery of Novel Materials (MARVEL), École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Theory and
Simulation of Materials (THEOS), Faculté des Sciences et Techniques de
l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Marco Borelli  National Centre for Computational Design and
Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Theory and Simulation of Materials
(THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Martin
Uhrin  National Centre for Computational Design and Discovery of Novel
Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015
Lausanne, Switzerland  Theory and Simulation of Materials (THEOS), Faculté des
Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Sebastiaan P. Huber  National Centre
for Computational Design and Discovery of Novel Materials (MARVEL), École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Theory and
Simulation of Materials (THEOS), Faculté des Sciences et Techniques de
l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Spyros Zoupanos  National Centre for Computational Design and
Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Theory and Simulation of Materials
(THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Carl S.
Adorf  National Centre for Computational Design and Discovery of Novel
Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015
Lausanne, Switzerland  Theory and Simulation of Materials (THEOS), Faculté des
Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Casper W. Andersen  National Centre
for Computational Design and Discovery of Novel Materials (MARVEL), École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Theory and
Simulation of Materials (THEOS), Faculté des Sciences et Techniques de
l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Ole Schütt  National Centre for Computational Design and
Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  nanotech@surfaces laboratory, Swiss
Federal Laboratories for Materials Science and Technology (Empa), CH-8600
Dübendorf, Switzerland  Carlo A. Pignedoli  National Centre for Computational
Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale
de Lausanne, CH-1015 Lausanne, Switzerland  nanotech@surfaces laboratory,
Swiss Federal Laboratories for Materials Science and Technology (Empa),
CH-8600 Dübendorf, Switzerland  Daniele Passerone  National Centre for
Computational Design and Discovery of Novel Materials (MARVEL), École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
nanotech@surfaces laboratory, Swiss Federal Laboratories for Materials Science
and Technology (Empa), CH-8600 Dübendorf, Switzerland  Joost VandeVondele
National Centre for Computational Design and Discovery of Novel Materials
(MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Swiss National Supercomputing Centre (CSCS), CH-6900 Lugano,
Switzerland  ETH Zürich, Switzerland  Thomas C. Schulthess  National Centre
for Computational Design and Discovery of Novel Materials (MARVEL), École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Swiss
National Supercomputing Centre (CSCS), CH-6900 Lugano, Switzerland  ETH
Zürich, Switzerland  Berend Smit  National Centre for Computational Design and
Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de
Lausanne, CH-1015 Lausanne, Switzerland  Laboratory of Molecular Simulation
(LSMO), Institut des Sciences et Ingenierie Chimiques, Valais, École
Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland  Giovanni Pizzi∗
National Centre for Computational Design and Discovery of Novel Materials
(MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland  Theory and Simulation of Materials (THEOS), Faculté des Sciences
et Techniques de l’Ingénieur, École Polytechnique Fédérale de Lausanne,
CH-1015 Lausanne, Switzerland  Nicola Marzari∗ National Centre for
Computational Design and Discovery of Novel Materials (MARVEL), École
Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland  Theory and
Simulation of Materials (THEOS), Faculté des Sciences et Techniques de
l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne,
Switzerland

###### Abstract

Materials Cloud is a platform designed to enable open and seamless sharing of
resources for computational science, driven by applications in materials
modelling. It hosts 1) archival and dissemination services for raw and curated
data, together with their provenance graph, 2) modelling services and virtual
machines, 3) tools for data analytics, and pre-/post-processing, and 4)
educational materials. Data is citable and archived persistently, providing a
comprehensive embodiment of the FAIR principles that extends to computational
workflows. Materials Cloud leverages the AiiDA framework to record the
provenance of entire simulation pipelines (calculations performed, codes used,
data generated) in the form of graphs that allow to retrace and reproduce any
computed result. When an AiiDA database is shared on Materials Cloud, peers
can browse the interconnected record of simulations, download individual files
or the full database, and start their research from the results of the
original authors. The infrastructure is agnostic to the specific simulation
codes used and can support diverse applications in computational science that
transcend its initial materials domain.

## Introduction

Core to the mission of open computational science is the principle that open
access to data, software and, eventually, infrastructure leads to scientific
results that can be assessed, verified and reproduced. While this principle
has long been at the foundation of science, information technology keeps
pushing the limit of what is possible, giving rise to the continuously
evolving challenge of translating this principle into practice in a
sustainable manner. Fortunately, funding agencies are increasingly aware of
the need to develop comprehensive solutions, including adequate data
management plans, [1, 2, 3, 4, 5] and guidelines are being developed to help
ensure that shared resources are easily findable, accessible, interoperable
and re-usable (FAIR). [6]

We believe this challenge calls for _open-science platforms_ that let
scientists use existing data, submit new content and launch new simulations
with minimal requirements on technical expertise. In this context, it is
instructive to look at the field of software engineering, where platforms for
sharing source code, such as GitHub (github.com), Bitbucket (bitbucket.org),
or GitLab (gitlab.com) have already revolutionised the industry – not only in
terms of the volume of source code that is shared publicly, but also in terms
of how software developers interact and write code. These platforms are
organised around Git, a software for “tracking changes in computer files and
coordinating work on those files among multiple people”[7]. Besides hosting
source code repositories, the platforms add a rich web interface for
interactive browsing, controlling workflows, and collaboration through social
interactions (sharing, commenting, mentioning, etc.). In our view, open-
science platforms can learn from these successful examples, and have the
potential to revolutionise the scientific discourse in similar ways. While
these considerations apply to computational science in general, in the
following we focus on the domain of materials.

The field of computational materials science is blessed in that research data
in the field is produced in digital form by default, and many of the necessary
computational tools are available free of charge under open-source licenses.
Over the last decade, substantial progress has been made in opening access to
some of these resources: An early example is nanoHUB [8], which provides
access to interactive simulation tools as well as educational materials in the
browser. Platforms have emerged that integrate data repositories with the
software frameworks used to compute the data, such as AFLOWlib [9] (with
aflow), the Materials Project [10] (with pymatgen, custodian, fireworks,
atomate), OQMD [11] (with qmpy), and the Open Materials Database [12] (with
httk). Finally, there are data repositories, such as NOMAD [13], that collect
and centralise large numbers of individual materials science calculations in
one place.

However, the field still faces challenges in the context of open science.
Materials simulations often rely on complex workflows, which, e.g., combine
simulations operating at different length- and time-scales or involve cycles
of post-processing followed by further simulations. This calls for a flexible
approach to designing such workflows, and to recording their many steps and
interconnected results. Furthermore, screening a class of materials, even for
one specific application, may involve running such workflows for thousands of
candidate materials or more and require substantial computational power – the
field of computational materials science is among the top consumers of high-
performance computing resources around the world [14, 15]. This makes an
efficient and complete record of the workflow execution highly valuable.

In our view, an open-science platform (OSP) should:

  1. 1.

support and adopt open simulation codes and analytics tools;

  2. 2.

provide an open architecture for defining and managing computational
workflows;

  3. 3.

offer turnkey solutions based on open workflows and curated open datasets that
are accessible to a diverse user base from computational science, experiments,
and industrial R&D; and

  4. 4.

enable FAIR sharing of data and workflows, facilitating reproducibility and
encouraging extension and/or modification of published resources.

With this vision in mind, we have designed and implemented the Materials Cloud
platform (materialscloud.org), which we describe in the remainder of this
paper.

## Results

Figure 1: Materials Cloud organises its resources in five sections, LEARN,
WORK, DISCOVER, EXPLORE, and ARCHIVE, representing different stages of the
research life cycle.

Materials Cloud with its five sections – LEARN, WORK, DISCOVER, EXPLORE, and
ARCHIVE – aims to provide an ecosystem that supports researchers throughout
the life cycle of a scientific project, and helps them make their research
output FAIR and reproducible. Fig. 1 illustrates how the five sections of
Materials Cloud mirror the typical research cycle, from learning to simulating
and finally publishing curated results, which become the starting point for
new research: LEARN (described in section Education and outreach: LEARN and
Quantum Mobile) contains educational materials and videos; WORK (section
Simulation services: WORK and AiiDA lab) focuses on simulation services,
turnkey solutions and data analytics tools. The three sections DISCOVER,
EXPLORE, and ARCHIVE are Materials Cloud’s approach to FAIR sharing of
research data (sections FAIR data: ARCHIVE and DISCOVER and Reproducibility
beyond FAIR: EXPLORE).

Materials Cloud is powered by AiiDA, a workflow manager for computational
science with a strong focus on provenance, performance and extensibility [16,
17]. AiiDA plays two roles in this context: that of a manager of simulations,
and that of a “stenographer” of events. The manager lets scientists interact
seamlessly with any number of remote high-performance computing (HPC)
resources, and orchestrates computational workflows involving many steps,
codes, and possible paths. The stenographer records the data trail leading
from the inputs to the results of a workflow, the _data provenance_ , and
stores it in databases tailored for efficient data mining of heterogeneous
results. Any such database can then be uploaded to the Materials Cloud, e.g.,
accompanying the submission of a scientific article, providing a comprehensive
record of the research project.

While trying to ingest all results into one monolithic database provides
advantages in terms of interoperability and data mining, it involves defining
a schema which all future contributions need to fit into and adapt. Materials
Cloud avoids this limitation by adopting the “repository of repositories”
model of GitHub et al., providing each submission with its own space. By using
the AiiDA provenance model, Materials Cloud contributors nevertheless benefit
from a unified user experience for browsing and searching for data and
simulations. They can rely on standardised AiiDA data types, where
appropriate, while AiiDA’s flexible plug-in system allows to add new types or
to extend existing ones to fit the specific purpose of the research
undertaken.

Specifically, the ARCHIVE is a moderated repository, where researchers can
submit relevant research data from computational materials science in formats
of their choice, including (but not limited to) AiiDA provenance graphs. The
repository guarantees long-term storage of records and associated metadata,
their findability via persistent identifiers, and their accessibility via
standard protocols. The ARCHIVE can also form the basis for additional,
interlinked layers of accessibility, interoperability and reusability:
DISCOVER allows researchers to adds curated visualisations for their data,
providing intuitive interfaces and context, while EXPLORE provides access to
the underlying raw and complete AiiDA provenance via an interactive graph
browser. In this model, AiiDA plays a role similar to Git (by tracking
materials science simulations) while Materials Cloud plays the role of GitHub
(a platform to share, browse and visualise all that has been tracked by
AiiDA).

In the following, we present the individual sections of Materials Cloud in
detail.

### FAIR data: ARCHIVE and DISCOVER

The ARCHIVE and DISCOVER sections allow researchers to make their data
available in a findable, accessible, interoperable, and reusable (FAIR) way
[6]. The Materials Cloud ARCHIVE is an open-access, moderated repository for
research data in computational materials science that allows researchers
worldwide to upload and publish their data free of charge. In particular:

  * •

it provides globally unique and persistent digital object identifiers (DOIs)
for every record;

  * •

metadata are always publicly available (Creative Commons Attribution Share-
Alike 4.0 license);

  * •

metadata can be harvested in a number of machine-readable formats, including
HTML meta tags (Dublin core), OAI-PMH (Dublin core) and JSON-LD (schema.org);

  * •

all data are stored at the Swiss National Supercomputing Centre;

  * •

it is non-commercial and free of charge;

  * •

data records are guaranteed to be preserved for at least 10 years after
deposition;

  * •

current size limits are 5 GB for general data records and 50 GB for AiiDA
databases;

  * •

moderators can approve larger data sets upon request (currently, 0.5 petabytes
are allocated overall, with a 10-year retention time per record).

Data management plans (DMPs) that describe the handling of data both during a
research project and after its completion are becoming standard components of
applications for research grants. The Materials Cloud ARCHIVE is listed on the
re3data[18] and FAIR sharing[19] repository registries, indexed by Google
Dataset Search and B2FIND (b2find.eudat.eu), and it is a recommended
repository for materials science by Nature Scientific Data.[20] It complies
with the data repository requirements of major funding agencies, and provides
tailored DMP templates (materialscloud.org/dmp).

Unlike _interdisciplinary_ repositories for research data, such as Zenodo
(zenodo.org), Data Dryad (datadryad.org), the Open Science Framework (osf.io),
or figshare (figshare.com), the ARCHIVE is _moderated_ and focuses on
providing added value for datasets from computational materials science.
Submissions to the ARCHIVE are expected to provide data that is of value to
and can be used by other researchers in the field, such as data supporting a
past, present or future peer-reviewed paper. Materials Cloud moderators are
subject experts, who follow a set of criteria (materialscloud.org/moderation)
to flag unsuitable or duplicate content, inappropriate form or topic, or
excessive submission rates, much in the spirit of the arXiv preprint server
(arxiv.org). While all data formats are accepted, moderators will suggest
alternative formats, where applicable, that improve interoperability and
reusability, in line with the 5-star deployment scheme to open web data
(5stardata.info).

Researchers can leverage the full power of the approach by adding interactive
DISCOVER and EXPLORE interfaces to their datasets in order to provide further
layers of accessibility, interoperability and reproducibility (see also
section Reproducibility beyond FAIR: EXPLORE below). DISCOVER sections focus
on curated data, presented in the form of dedicated interactive
visualisations. For example, in the DISCOVER section “ _2D structures and
layered materials_ ” [21], users can browse the curated dataset discussed in
Ref. [22]. After selecting a material, key properties of the compound are
displayed on a detail page (Fig. 2a,b), which includes interactive
visualisations of quantities, such as the crystal structure, the electronic
band structure, as well as phonon eigenvectors and band structures. Fig. 3
shows screenshots from another DISCOVER sections on “ _Covalent organic
frameworks (COFs) for methane storage applications_ ” [23], containing
interactive versions of the static figures published in reference [Rocio2018].
The research data underlying all DISCOVER sections on Materials Cloud is
published in corresponding ARCHIVE records and citable through DOIs.

What differentiates Materials Cloud DISCOVER sections from other approaches to
presenting materials data, is that each piece of data in a DISCOVER section
can be linked to a node in the AiiDA provenance graph (Fig. 2c) for full
reproducibility, as we discuss in the following.

Figure 2: DISCOVER section on “ _2D structures and layered materials_ ” [21].
The “ID card” of a material displays key computed properties, as well as
interactive visualisations of the crystal structure (a), the electronic band
structure (b) and more. AiiDA icons link every piece of data to its
corresponding node in the provenance graph that can be browsed through the
EXPLORE interface (c).  Figure 3: DISCOVER section on “ _Covalent organic
frameworks (COFs) for methane storage applications_ ” [23], presenting almost
70000 COFs assembled _in silico_ , together with their computed properties (a)
and atomic structures (b) in the form of interactive figures that mirror those
published in the corresponding peer-reviewed paper.

### Reproducibility beyond FAIR: EXPLORE

While making data FAIR simplifies and accelerates the sharing of knowledge, it
is equally important to ensure that the knowledge being shared is reliable.
Computational materials science involves running computer programs on digital
inputs and producing digital outputs. Yet, historically, only _some_ input and
output data have been shared in the computational materials science
literature, often in narrative form, making it unnecessarily difficult for
peers to reproduce reported results. While storing and sharing _all_ data may
not be technically feasible or financially sensible, researchers (and
reviewers) today should demand that the data provided is sufficient to
reproduce the reported results in their entirety.

This simple and seemingly self-evident demand can be tedious and time-
consuming to meet in practice. Researchers leave out pieces of information for
a variety of reasons: data may appear trivial, irrelevant or too complex to
provide in accessible form. The challenge of providing access to this data is
amplified, e.g. in studies involving large numbers of materials or workflows
with many different steps, and calls for tools that simplify and automate this
task.

In AiiDA, the “stenographer” records, for every calculation, a set of
standardised data and metadata in a dedicated database [24]. This includes
information on who submitted the calculation, when the calculation was
submitted, which computer and code were used, which inputs were used, which
outputs were produced, as well as how these outputs are further used as inputs
to the next calculation (see also Fig. 4). Since long-term data storage is
more expensive than the short-term storage used by active simulations, it is
often not reasonable to preserve all output data. Which output data is stored
is decided by the AiiDA plug-in for the code in question – for example, in a
density-functional theory calculation, total energies, electronic band
structures and log files might be stored by default, while Kohn-Sham wave
functions might be discarded. The overarching principle, however, is that _all
information needed to reproduce the outputs must be preserved_ , even if not
all intermediate files are persisted. By combining this information stored at
the level of individual calculations with the logical relationships between
successive calculations, AiiDA provides reproducibility of entire workflows
out of the box.

Scientists who use AiiDA for their calculations can choose to upload their
AiiDA databases to the EXPLORE section in order to complement their published
research with a complete record of their calculations. When they do so, peers
can browse the AiiDA graph as shown in Fig. 4: all nodes of the graph,
representing either calculations or pieces of data, can be inspected and are
linked to their parent and children nodes via the provenance browser.
Dedicated visualisations make the content of nodes intuitively accessible, and
allow to download individual pieces of data (such as crystal structures or
input files). Subject experts, on the other hand, can install AiiDA on their
computer, import the AiiDA database and continue their own research from where
the authors of the original work left off.

Figure 4: EXPLORE interface for AiiDA provenance graphs. (a) Interactive view
of a calculation node (here representing a run of pw.x code from the Quantum
ESPRESSO suite [25]), providing download links for all input and output files.
The provenance browser on the right allows to jump to the visualisation of any
input or output node of the calculation. (b) Interactive view of the atomic
crystal structure returned by calculation (a). The provenance browser
indicates this structure was used in three subsequent calculations. See the
supporting information for the full provenance graph.

We note that the interactive provenance browser is not limited to datasets
uploaded to Materials Cloud: AiiDA users can connect their own database to the
EXPLORE JavaScript application (via AiiDA’s built-in REST API) and directly
browse their own database without their data ever leaving their computer.

### Simulation services: WORK and AiiDA lab

While DISCOVER, EXPLORE, and ARCHIVE enable the dissemination of results that
have already been computed, WORK aims to facilitate data generation and
analytics by means of simulation tools and services. The WORK section
leverages web technologies in order to make well-defined calculations and
workflows simple to run and accessible to a wide user base, including
students, experimental scientists, and computational scientists.

On the one hand, this includes stand-alone tools that run computationally
inexpensive simulations, which produce immediate results: for example, tools
that help with plotting electronic band structures (Fig. 5a) or visualising
lattice vibrations (Fig. 5b), and several tools leveraging machine learning
methods. The underlying docker technology (docker.com) makes it possible to
support a diverse set of software frameworks on the same platform, allowing
for custom solutions that are adapted to the specific tool in question.

Figure 5: Tools in the WORK section. (a) SeeK-path tool [26] for finding and
visualising paths in reciprocal space, here showing the Brillouin zone of
InHg. (b) Interactive visualiser for lattice vibrations (adapted from
henriquemiranda.github.io/phononwebsite), here for two-dimensional
phosphorene. Shown is the phonon eigenvector (left) corresponding to the red
dot in the phonon band structure (right).

On the other hand, the WORK section focuses on the AiiDA lab, an ecosystem for
applications powered by the AiiDA workflow manager
(materialscloud.org/aiidalab). AiiDA lab aims to remove barriers related to
the set up and installation of simulation software by providing access to
applications for launching and controlling computational workflows directly
from the web browser. Users log on to a private, containerised environment
that provides a persistent work space for storing apps, the AiiDA database,
and file repositories (Fig. 6a,b). AiiDA lab apps let users connect to their
own computational resources anywhere in the world in order to run production-
grade workflows. Users can import data into the platform either by uploading
from their computer, or by importing data from connected open databases such
as the Crystallography Open Database [27] or any database implementing the
OPTIMADE standard (optimade.org), including AFLOW (aflow.org [9]), COD
(crystallography.net/cod [27]), TCOD (crystallography.net/tcod [28]),
Materials Cloud, MPDS (mpds.io [29]), Materials Project (materialsproject.org
[10]), NOMAD (nomad-coe.eu [13]), Open Materials Database
(openmaterialsdb.se[12]), and OQMD (oqmd.org [11]).

The intuitive graphical interface makes AiiDA lab applications an ideal
vehicle for sharing turnkey solutions with non-specialists, be it
computational scientists from another discipline or experimental researchers
with no programming experience.

Figure 6: AiiDA lab simulation environment. (a) Landing page with an overview
of the applications installed. (b) “App store” for managing applications. (c)
Application that computes the optimised crystal structure of an input material
as well as its electronic band structure along standardised paths. Clicking
“Edit App” switches to the source code editor (d) of the underlying Jupyter
notebook.

From a technological perspective, AiiDA lab applications are Jupyter notebooks
(jupyter.org) containing instructions for the AiiDA workflow manager, which
are transformed into an interactive web application (see Fig. 6c,d). This
design has two important implications for app _development_ : First, the
widespread adoption of Python and Jupyter notebooks in data science in general
[30] and computational materials science in particular makes most researchers
in the field potential app developers. In particular, thanks to Jupyter
widgets, interactive web interfaces can be written in a few lines of Python,
and no longer require knowledge of JavaScript). And second, AppMode lets
developers switch between the graphical app layout (Fig. 6c) and the Python
development environment (Fig. 6d) at the click of a button. Apps can be edited
live in the browser, and developers have the full power of the Python
programming language at their fingertips.

AiiDA lab encourages sharing of workflows and visualisations via an App store
model: in a first step, developers register their application on the
application registry (aiidalab.github.io/aiidalab-registry). Once registered,
users can then install the app via the built-in application manager (Fig. 6b)
and access it from their home screen (Fig. 6a). The source codes of the AiiDA
lab, AppMode, and AiiDA itself are released under the permissive MIT open-
source license (see code availability statement), enabling re-deployment of
the AiiDA lab platform both in academic and in corporate environments.

When a local installation is desired, e.g., for educational purposes, users
can download the Quantum Mobile virtual machine (see section Education and
outreach: LEARN and Quantum Mobile for a full description), which provides the
same environment in a self-contained form and runs on Linux, MacOS, and
Windows.

### Education and outreach: LEARN and Quantum Mobile

The LEARN section of Materials Cloud hosts video lectures, tutorials, and
seminars in computational materials science (Fig. 7a). Lectures in
collaboration with CECAM (cecam.org) include the “Classics on Molecular and
Materials Simulations”, dedicated to record pioneering contributions in the
field, and the “Mary Ann Mansigh conversations” in which outstanding
representatives from computational science share their perspective on how
modelling affects society. Videos are grouped by topic or event, presented
together with accompanying materials, and slides where available. The
Slideshot video player shows video and slides side by side, and keeps them in
sync (Fig. 7b, slideshot.epfl.ch).

Figure 7: Education and outreach. (a) MARVEL distinguished lectures available
in the LEARN section. (b) Slideshot player with slide synchronisation and
slide-based browsing. (c) Simulation codes provided with the Quantum Mobile
virtual machine, and deployment schemes. (d) Screenshot of the Quantum Mobile
desktop.

Besides the educational materials in the LEARN section, students can also
download the Quantum Mobile virtual machine for computational materials
science (Fig. 7c) from the WORK section. Quantum Mobile is based on Ubuntu
Linux and comes pre-installed with a collection of open-source software
packages for quantum-mechanical calculations including Quantum ESPRESSO [25],
Yambo [31], fleur [32], Siesta [33], CP2K [34], and Wannier90 [35].

Furthermore, it includes the Standard Solid State Pseudopotential Library
(SSSP) [36, 37], various visualisation tools (jmol [38], XCrySDen [39],
gnuplot, grace), a job scheduler (Slurm) and a build environment with C, C++
and Fortran compilers as well as scientific and MPI libraries. AiiDA and the
AiiDA lab environment are pre-configured, including AiiDA plug-ins for each of
the ab initio codes listed above, ready to be used out-of-the-box (Fig. 7c,d).

Quantum Mobile provides a uniform environment for quantum mechanical materials
simulations and runs on most popular operating systems, including Linux, MacOS
and Windows, via the VirtualBox software (virtualbox.org). Contrary to other
encapsulation strategies, such as Docker, students interact with a familiar
graphical desktop, shown in Fig. 7d. Since its first release in November 2017,
Quantum Mobile has been continuously updated and was used in lecture courses
at EPFL, ETHZ, and Ghent University (compmatphys.org) as well as in numerous
tutorials on electronic structure methods, molecular simulations, and AiiDA
(see materialscloud.org/quantum-mobile), where it helps to reduce the time
needed for installation and configuration of software.

The modular design of Quantum Mobile takes into account that one size does not
fit all: its components (simulation codes, tools, data) are encapsulated in
reusable, individually tested components (see Code availability statement).
Teachers can pick and choose from a growing repository of more than 30 roles
and build their own version of Quantum Mobile containing just the tools they
need.

## Discussion and Outlook

The increasing availability and standardisation of infrastructure-as-a-service
(IaaS) make it possible to share the findings and capabilities developed by
computational materials science not only with peers who possess journal
subscriptions and specialist software, but with anyone familiar with using a
web browser. In the case of Materials Cloud, this includes (i) the
interconnected outcomes of calculations and workflows, presented in a
findable, accessible, interoperable, reusable, and reproducible way (DISCOVER,
EXPLORE, and ARCHIVE sections), as well as (ii) turnkey solutions that launch
state-of-the-art workflows at the click of a button (WORK section).

Materials Cloud and AiiDA form the core of the open science platform used at
the National Centre on Computational Design and Discovery of Novel Materials
(MARVEL NCCR, started in 2014), funded by the Swiss National Science
Foundation, the Centre of Excellence for Materials Design at the Exascale
(MaX, started in 2015), funded by the European Commission, as well as further
partner projects (materialscloud.org/home#partners). Since its official launch
in early 2018, Materials Cloud has grown steadily as it becomes the central
repository for sharing research data, workflows, and tools within MARVEL, MaX,
and further partner projects. Today, the Materials Cloud ARCHIVE provides a
moderated repository for the long-term storage of materials science research
data, open to submissions from around the world. For AiiDA lab, we propose a
model where interested parties, such as academic institutes, research centres,
and companies can re-deploy the open-source platform on their own (virtual)
hardware.

While the content on Materials Cloud can indeed be _used_ simply through a web
browser, _submitting_ new tools and interactive visualisations still requires
technical expertise. We are working both on lowering this barrier and on
reducing the associated workload from platform administrators by moving in the
direction of a platform-as-a-service architecture. The submission procedure
and interface of the ARCHIVE will soon be further streamlined by the switch to
the Invenio framework (invenio-software.org), bringing user authentication,
search and more. Finally, the governance model of Materials Cloud will evolve,
adapting to its increasing role within the MARVEL and MaX scientific centres,
and the computational materials science community at large.

One unresolved challenge in the field is the task to find a common language
for information exchange between OSPs. Efforts to move forward in this
direction range from collecting existing semantic assets in computational
materials science [40], over the design of new ontologies [41], to
specifications of interoperable data formats [42, 43], and application
programming interfaces (optimade.org). Once these efforts converge, they can
be connected to existing infrastructures for structured web data (schema.org).

Another important challenge is to secure long-term support for continued
development. The diversity of relevant services goes far beyond the long-term
storage of files and requires maintenance. Analogies can be drawn to other
major research infrastructures, ranging from particle accelerators over
telescopes to libraries, where key services are provided to the scientific
community, either by the public or in the form of public-private partnerships.
Given the unprecedented availability of computational power (top500.org), the
pervasiveness of computational (materials) science in the scientific
literature [44], and its relevance to pressing societal challenges [45],
maintaining functional research infrastructures for computational science – at
comparatively low cost – would seem like a forward-looking investment.

## Methods

Figure 8: Materials Cloud architecture diagram. Independent frontends for
LEARN, WORK, DISCOVER, and EXPLORE based on AngularJS are powered by different
backends, including AiiDA REST APIs, tools encapsulated in docker containers
and a JupyterHub running one docker container per AiiDA user.

Materials Cloud’s modular architecture, sketched in Fig. 8, is designed to
enable updates of individual sections without affecting the rest of the
service. The top-level web user interface is presented through a set of
AngularJS applications with one app per section. Section content is served
either directly by the corresponding application (LEARN, DISCOVER, EXPLORE) or
through user interfaces provided by containerised content (WORK, DISCOVER).
The overall Materials Cloud theme is based on the Bootstrap (getbootstrap.com)
and Material Design (material.angularjs.org) JavaScript libraries; and
individual sections use a range of visualisation libraries, including
Highstock/Highcharts (highcharts.com), D3js (d3js.org), JSMol
(jsmol.sourceforge.net/), and Vis (visjs.org).

A slideshot server provides the API to serve videos and slides to the LEARN
section. Tools in the WORK section are encapsulated in docker containers and
control their own web frontends. The AiiDA lab is a customised JupyterHub that
is isolated from the rest of the platform and runs on a separate server. Every
AiiDA lab account is associated with a private container, including persistent
storage and compute resources, and may be set up to connect to high-
performance computing resources owned by the account holder. Containerised
contributions to WORK and DISCOVER may use different Python-based frameworks,
such as Flask (flask.palletsprojects.com), Django (djangoproject.com), or
Bokeh (bokeh.org).

In the EXPLORE section, the frontend JavaScript application talks directly to
the standardised AiiDA application programming interface (API). This
representational state transfer (REST) API provides access to calculations,
workflows, codes, and data stored in the AiiDA graph, and makes them available
in the JavaScript Object Notation (JSON) format. The AiiDA REST API ships
together with AiiDA, and besides serving static AiiDA databases on the
Materials Cloud, AiiDA users can take advantage of the same JavaScript
application to browse their own local AiiDA database. For more details, see
Fig. S1 in the supplementary materials.

The ARCHIVE section is only loosely coupled to the rest of the platform. Files
associated with ARCHIVE records are stored in an OpenStack Swift Object Store
and backed up to tape daily (user.cscs.ch/storage/object_storage). The ARCHIVE
server hosts the database containing the metadata associated with records, and
delegates requests for associated files to the object store via short-lived
unique URLs. The current implementation is based on the Flask microframework,
but will transition in 2020 to a highly scalable infrastructure based on
Invenio 3, the open-source Python framework powering the Zenodo repository
operated by CERN.

Materials Cloud is deployed on virtual machines running in an OpenStack cloud
computing platform (openstack.org) at the Swiss National Supercomputing Centre
(CSCS). All production servers are duplicated, following standard web
development practises (see Fig. S2 in the supplementary materials for
details). In order to prevent loss of log files and user data, backups are
taken periodically and stored in the object storage service at CSCS. A server
at a different physical location monitors availability and basic functionality
of all production services every 60 seconds and notifies maintainers in case
of unexpected deterioration of service.

Deployment is automated using Ansible playbooks (ansible.com), which allow
software provisioning, configuration management, and application deployment on
remote machines over SSH. The use of automated Ansible roles, together with
Materials Cloud’s modular architecture and the widely available OpenStack
infrastructure, simplifies the redeployment of Materials Cloud (or components
of it) in other locations, e.g., for the purpose of load balancing, federation
of service, or in-house use.

### Data availability

The datasets discussed in this manuscript [21, 23], as well as the datasets
underlying all Materials Cloud DISCOVER and EXPLORE sections are available on
the Materials Cloud Archive (archive.materialscloud.org) under Creative
Commons licenses.

### Code availability

The source code of AiiDA, the AiiDA lab, Appmode
(github.com/oschuett/appmode), and most AiiDA lab applications is released
under the MIT open-source license, and made available under the aiidateam
(github.com/aiidateam) and aiidalab (github.com/aiidalab) GitHub
organisations.

The Quantum Mobile virtual machine can be downloaded from
materialscloud.org/quantum-mobile. Its source code (in the form of ansible
roles and playbooks) is released under the MIT license and made available
under the marvel-nccr GitHub organisation (github.com/marvel-nccr).

## Acknowledgements

This work is supported by the MARVEL National Centre for Competency in
Research funded by the Swiss National Science Foundation (grant agreement ID
51NF40-182892), the European Centre of Excellence MaX “Materials design at the
Exascale” (grant no. 824143), the “MaGic” project of the European Research
Council (grant agreement ID 666983), the swissuniversities P-5 “Materials
Cloud” project (grant agreement ID 182-008), the “MARKETPLACE” H2020 project
(grant agreement ID 760173), the “INTERSECT” H2020 project (grant agreement ID
814487), the “NFFA” H2020 project (grant agreement ID 654360), the “EMMC”
H2020 project (grant agreement ID 723867). We acknowledge PRACE for awarding
us simulation time on Piz Daint at CSCS (project ID 2016153543) and Marconi at
CINECA (project ID 2016163963), the Swiss Platform for Advanced Scientific
Computing PASC for the SIRIUS co-design activities, and EPFL and the Swiss
National Science Foundation for supporting our long-term data storage needs.

We thank the IaaS support teams at the Swiss National Supercomputing Centre
(CSCS): Sadaf Alam, Vincenzo Annaloro, Marco Consoli, Pablo Fernandez, Stefano
Gorini, Hussein Harake, Guy-Maël Horclois Le Pironnec, Mark Klein, Giuseppe Lo
Re, Colin McMurtrie and Marco Passerini. And, finally, we would like to thank
the early contributors to Materials Cloud for helping shape the project
through their feedback and submission of tools, of DISCOVER and of EXPLORE
sections: Nicolas Mounet, Antimo Marrazzo, Nicolas Hörmann, Gianluca Prandini,
Yoyo Hinuma, Félix Musil, David M. Wilkins, Michele Ceriotti, Bonan Zhu,
Henrique Miranda, Thibault Sohier, Mohammad Moosavi, QuanSheng Wu, Oleg
Yazyev, Benjamin Meyer, Clémence Corminboeuf, Kevin M. Jablonka, Daniele
Ongari, and Jinhui Guo.

## Author contributions

GP and NM conceived the project.

FG, LT, MU, BS, GP, and NM designed the structure of the Materials Cloud.

LT, SK, FG, OS, MU, SPH, and GP designed the software and service
architecture.

SK, LT, AVY, EP, OS, MB, VG, FG, and GP implemented and deployed the platform.

SK, EP, AVY, MB, LT, VG and GP form the Materials Cloud Team currently
maintaining the platform.

SPH, SZ, CWA, CSA and MU supported the Materials Cloud through development of
required features in the AiiDA framework.

BS, CAP, DP, LT, GP, and NM supervised the implementation of different aspects
of the platform.

JV, LT, GP and TCS coordinated integration of the platform with infrastructure
at CSCS.

All authors discussed the manuscript.

## Competing interests

The authors declare no competing interests.

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##  Appendix A Supporting Information

Figure S1:  Data flow between Materials Cloud frontend and backend. Left: Data
flow for ARCHIVE, DISCOVER and WORK. Right: AiiDA REST API, flow starting from
the browser request (top) via data validation, parsing and translation into
the AiiDA query language down to the database query (bottom). Response data
then follows the reverse path and is returned to the browser in JSON format.
Figure S2: Materials Cloud deployment diagram. Services are split across
multiple virtual machines, each with at least one clone for development and
testing.  Figure S3: Complete AiiDA provenance graph for the electronic band
structure calculation of Bi2Se2Te, as generated by verdi node graph generate
4638bfc1 on the AiiDA database from reference [21].