# Text-mined dataset of gold nanoparticle synthesis procedures, morphologies,
and size entities

Kevin Cruse1,2, Amalie Trewartha2, Sanghoon Lee1,3,
Zheren Wang1,2, Haoyan Huo1,2, Tanjin He1,2, Olga Kononova1,2,
Anubhav Jain3, Gerbrand Ceder1,2*

1Department of Materials Science and Engineering, University of California,
Berkeley, CA 94720, USA;
2Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley,
CA 94720, USA;
3Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA
94720, USA;
*Corresponding author: Gerbrand Ceder<EMAIL_ADDRESS>

## Abstract

Gold nanoparticles are highly desired for a range of technological
applications due to their tunable properties, which are dictated by the size
and shape of the constituent particles. Many heuristic methods for controlling
the morphological characteristics of gold nanoparticles are well known.
However, the underlying mechanisms controlling their size and shape remain
poorly understood, partly due to the immense range of possible combinations of
synthesis parameters. Data-driven methods can offer insight to help guide
understanding of these underlying mechanisms, so long as sufficient synthesis
data are available. To facilitate data mining in this direction, we have
constructed and made publicly available a dataset of codified gold
nanoparticle synthesis protocols and outcomes extracted directly from the
nanoparticle materials science literature using natural language processing
and text-mining techniques. This dataset contains 5,154 data records, each
representing a single gold nanoparticle synthesis article, filtered from a
database of 4,973,165 publications. Each record contains codified synthesis
protocols and extracted morphological information from a total of 7,608
experimental and 12,519 characterization paragraphs.

## Background & Summary

The synthesis of gold nanoparticles has been practiced for centuries, and
their modern applications are widespread, which include in vitro
diagnostics[1], semiconductor technology[2], and cosmetics[3]. The application
of gold nanoparticles often depends on their morphology and size[4]; yet,
despite their ubiquity, only relatively recently has the control of these
properties been interrogated systematically[5].

While many theories and models exist for the mechanisms that determine
nanoparticle morphology [6, 7, 8], most of the exploration of this synthesis
space is driven by heuristics. For nanorod growth in particular, it appears
that the simultaneous presence of many reagents affects the final
characteristics of a sample of gold nanorods[9]. While factorial experiments
can offer some insights into how varying certain precursor concentrations
affects final particle morphology, size, or aspect ratio, it is impractical to
perform enough experiments to cover a large enough portion of the synthesis
space to produce an effective model, even with state-of-the-art high-
throughput synthesis methods.

Beyond empirical modeling and experiment, computational methods exist that
either simulate the energetics of the formation of nanoparticles or
interrogate the nucleation and growth steps traversed by nanoparticles.
However, these approaches come with inherent tradeoffs between the resolution
of atomic interaction and computational tractability. For example,
calculations from first-principles have been conducted using density
functional theory (DFT) that probe the energetic landscape of potential gold
nanoparticle shapes[10], including the effects of various surface ligands[11],
which are vital for the synthesis of solution-phase noble metal
nanoparticles[12]. However, such a technique does not take into account the
intricacies of nucleation and growth competition in solution-based
nanoparticle synthesis. On the other hand, continuum-level models can
represent real-time growth and dispersity dynamics[13], though sacrificing the
small-scale energetics highlighted by techniques such as DFT.

In a third paradigm of scientific investigation, the volume of data-driven
approaches to understanding chemistry and materials synthesis is accelerating.
These approaches represent a resourceful complement to established
computational methods and raw experimentation, and have been proven successful
in applications such as materials discovery [14, 15], synthesis protocol
querying [16], and the simulation and interpretation of characterization
results [17, 18]. However, data-driven approaches are limited by the
completeness and substance of the data resource(s) used. For the
nanotechnology field, Yan et al. [19] constructed a table of available
nanotechnology databases, their volume, as well as a description of their
proposed usages. Each of these, including the dataset created by Yan et al.,
provide useful and carefully curated information on the characteristic
features of various nanostructures; however, to our knowledge, none provide
validated protocols for their synthesis.

There is a wealth of gold nanoparticle synthesis and characterization data
available for data-driven approaches in unstructured form in the scientific
literature [20]. This source remains largely untapped because manually
extracting such information is both tedious and unrealistic due to the volume
of the literature. Natural language processing (NLP) and text-mining
techniques have been successfully employed and established in scientific
fields related to materials science [21, 22, 23], and the sub-field of text-
mining in materials science is budding [24, 25, 26, 27]. Here we employ
natural language processing (NLP) and text-mining techniques on a collection
of nearly 5 million materials science publications [28] to extract gold
nanoparticle synthesis recipes and their outcomes. We introduce this dataset
as an additional element, along with direct experimentation and computational
modeling, in the effort to capture the mechanisms for metal nanoparticle
growth.

In this data descriptor, we present an open-source dataset of 5,154 records
filtered down from a database of 4,973,165 publications, each representing a
single article and containing automatically extracted gold nanoparticle
synthesis recipes and morphological information from the contained synthesis
paragraphs and characterization paragraphs. Within a synthesis paragraph, the
precursors used and their amounts are extracted, and the synthesis actions and
conditions are extracted and codified into a procedure graph. Within both
synthesis paragraphs and characterization paragraphs, morphological (e.g.
“spherical”, “nanorod”, “dogbone-like”) and size (e.g. diameter, aspect ratio)
entities were extracted. In total, this dataset encompasses 7,608 synthesis
paragraphs and 12,519 characterization paragraphs. A schematic of the pipeline
devised for this extraction is shown in Figure 1.

## Methods

### Content Acquisition

Over 4.5 million publications from material science journals have been scraped
from the web and parsed by the process described in Kononova et al. [28].
These publications were obtained through agreements with publishers Elsevier,
Wiley, the Royal Society of Chemistry, Nature Publishing Group, the American
Institute of Physics, Springer, the American Chemical Society, the American
Physical Society, and the Electrochemical Society. Journals from each
publisher related to materials science were selected manually. Articles were
scraped from these publishers using a custom web scraping tool. Only articles
published after the year 2000 were collected because prior publications are
often provided in PDF and not HTML/XML format, the latter of which is more
straightforward to parse [24]. Parsers were built for each publishing group
using custom parsing toolkits. From each article, the full text and relevant
metadata were parsed and are stored across MongoDB database collections
(www.mongodb.com) maintained on an internal server.

### AuNP Synthesis Publication Collection

Publications containing gold nanoparticle synthesis procedures were collected
using a series of progressively finer-meshed filters, combining unsupervised
and supervised text processing methods. This progression is illustrated in
Figure 1, along with the corresponding yield of articles at each step. The
stages of the pipeline were built to facilitate adaptation to other
nanomaterial compositions in the future. Each of the steps is described in
detail below.

#### Identifying AuNP Publications

The early stages of the pipeline cast a wide mesh for Au nanoparticle (AuNP)
publications using unsupervised text-mining methods. These first two steps
were adapted from the methods used for nanomaterial synthesis publication
extraction performed by Hiszpanski et. al [20]. The first step consists of a
simple regular expression query. To accomplish this, we imported the
paragraphs of all papers from our MongoDB database (described in “Content
Acquisition”) into an Apache Solr search engine instance
(https://solr.apache.org/), which is powered by Apache Lucene and supports
fast full-text search. We used the default English Lucene analyzers and
tokenizers in Solr. The full text of every article was queried for words
starting with “nano”, followed by any number of characters or whitespace. The
query was written in Solr-specific query syntax and returned identifiers of
the matching paragraphs. We used the query results to lookup and fetch related
papers in the MongoDB database. This step yielded 757,807 nanomaterial
articles.

Next, all of the documents in this corpus were vectorized in a term frequency-
inverse document frequency (TF-IDF) representation, where each element in the
vector represents the frequency of a given token normalized by the frequency
of that term across documents [29]. We implemented this vectorization using
the scikit-learn TfidfVectorizer module (https://scikit-learn.org/stable/),
with tokenization from ChemDataExtractor’s ChemWordTokenizer [23], default
English stopwords eliminated, and a minimum document frequency of 100 for each
token. With the TF-IDF vectorized corpus, articles were collected whose TF-IDF
values for the words “gold” or “Au” were larger than any of “silver”, “Ag”,
“copper”, “Cu”, “palladium”, “Pd”, “platinum”, or “Pt”. 121,700 gold
nanomaterial articles were collected in this step, each being related to gold
nanomaterials but not necessarily containing protocols for their synthesis.

#### AuNP Synthesis Paragraph Classification

To isolate those AuNP publications that contain synthesis protocols, we
trained a transformer-based binary classification model using the Simple
Transformers NLP library
(https://github.com/ThilinaRajapakse/simpletransformers). We first pre-trained
a BERT [30] (Bidirectional Encoder Representations from Transformers) model
specializing in materials science text, referred to as “MatBERT” [31]. The
pre-training data for MatBERT were 2 million randomly sampled papers from our
publications database. Following the original BERT, we trained two WordPiece
tokenizers of vocabulary size 30,522 (cased and uncased) from scratch on this
full-text to optimize tokenization for materials science terminologies. After
all the papers were tokenized, paragraphs with less than 20 or more than 510
tokens were removed. Out of 61 million paragraphs from the 2 million sampled
papers, roughly 17% contained less than 20 tokens and about 2% contained more
than 510 tokens, for both the cased and uncased set of texts. This yielded
around 50 million paragraphs and 8.8 billion tokens. During pre-training,
MatBERT was trained for the masked language modeling (MLM) task, which
requires MatBERT to predict the original tokens in a paragraph after they are
masked. This pre-training step helps MatBERT to develop a general
understanding of the language and better learn the classification of synthesis
protocols. The training codes and pre-trained MatBERT models can be found at
https://github.com/lbnlp/MatBERT.

To gather positive training paragraphs, we first modeled the topics of every
paragraph in the aforementioned gold nanoparticle publication collection using
latent Dirichlet allocation (LDA) [32]. Then, we collected and manually
validated those paragraphs whose dominant topic was related to synthesis
(topic words including “synthesized”, “solution”, “ml”, “addition”, etc.). A
range of negative training paragraphs were collected manually from various
parts of a typical publication, including the introduction, results,
discussion, and characterization sections. Annotations were accomplished using
SpaCy’s Prodigy interface (https://prodi.gy). The training data ultimately
included 739 training examples, with 242 positive examples and 497 negative
examples. Because synthesis paragraphs are far less common in literature than
non-synthesis paragraphs, we included more negative than positive training
examples to ensure that most kinds of these non-synthesis paragraphs were
covered in the training data. Using the ClassificationModel module from Simple
Transformers, training data was split into 80/10/10 train/validation/test sets
and trained over 20 epochs. Articles were then identified that contained at
least one paragraph classified as being related to gold nanoparticle
synthesis. This step yielded 21,989 AuNP synthesis paragraphs from a total of
17,302 articles.

#### Synthesis Recipe Extraction

Synthesis targets, precursors, their amounts, synthesis actions, and action
conditions were all extracted using synthesis procedure extraction and
codification tools described in [28] and [33]. A sample of an example
extraction from a synthesis paragraph is shown in the bottom panel of Figure
1. Each step is described in detail below.

_Materials entity recognition (MER)._ To identify and classify targets,
precursors, and other materials from synthesis paragraphs, we implemented a
two-step model. In the first step, each word token was transformed into an
embedding vector with the MatBERT model (see “AuNP Synthesis Paragraph
Classification”). Then, the embedding vector was passed to a bi-directional
long-short-term memory neural network with a conditional random-field top
layer (BiLSTM-CRF) to identify whether the corresponding token was a materials
entity or a regular word. In the second step, each materials entity was
replaced with a keyword _< MAT>_ and classified as either a _target_ ,
_precursor_ , or _other_ material using another BERT-based BiLSTM-CRF network
with a similar structure. In total 1,281 synthesis paragraphs from 1,155
papers were annotated by labeling each word token as _material_ , _target_ ,
_precursor_ , or _outside_. The annotated dataset was split into
training/validation/test sets with a paper-wise ratio of 700/150/305 to train
the aforementioned two neural networks.

_Synthesis actions and their attributes._ To recognize and classify synthesis
actions described in a paragraph, we implemented an algorithm that combines a
recurrent neural network (RNN) and rule-based parsing of sentence dependency
trees. Sentences were tokenized using ChemDataExtractor’s ChemWordTokenizer.
The RNN performed classification of sentence tokens into 5 categories: _start-
synthesis_ (general actions that signify that something was synthesized, e.g.
“synthesized”, “prepared”, etc.), _mixing_ , _heating_ , _drying_ , and
_cooling_ , which are the basic actions in nanoparticle synthesis. The RNN was
trained on a set of 3,040 synthesis sentences from 535 synthesis paragraphs
(classified according to the paragraph classifier described in Huo et al.
[34]). In brief, 3,781 sentences were taken from 199 solid-state, 51 sol-gel,
148 hydrothermal, and 137 precipitation synthesis paragraphs. 3,040 sentences
in this set were determined to be synthesis sentences (as opposed to
characterization or miscellany). The tokens in these 3,040 synthesis sentences
were annotated by human experts in NLP and materials synthesis science
according to their type of synthesis action, with the actions relevant to
nanoparticle synthesis listed above. The tokens’ feature vectors were
generated using a Word2Vec model [35]. The embeddings were trained on
$\sim$400,000 synthesis paragraphs of different synthesis types using the
Gensim library [36]. The sentences of paragraphs were lemmatized, all the
quantity tokens were replaced with the keyword <NUM>, and all the chemical
formulas were replaced with the keyword <CHEM> using rule-based algorithms.
The SpaCy library [37] was used to grammatically parse each sentence and
obtain linguistic features of the tokens, such as their part of speech and
their dependency on root tokens. For training, validation and testing, the
annotated set was split into a 70/10/20 fraction, respectively. Synthesis
action attributes, such as temperature, time, and environment were extracted
by using dependency tree parsing and a rule-based regular expression approach
[29].

_Material quantities extraction._ To correlate the numerical values of
material quantities, such as molarity, concentration, or volume, to materials
entities extracted by the MER model (see above), we applied a rule-based
approach. First, we used the NLTK library [38] to build syntax trees [29] for
each sentence in a paragraph, where every word is represented as a leaf node.
Then, the syntax tree for each sentence was cut into the largest sub-trees for
every material, with each sub-tree having only one material entity. To do
this, we first identified the materials on leaf nodes. Then, starting from
each material, we identified the largest sub-trees (i.e., we traversed the
syntax tree upwards until there was more than one material leaf node
descending from the same node). Finally, the largest sub-tree for a given
material was defined as the sub-tree formed by the node and its descendants
identified in the previous step. Next, we searched for the quantities in each
sub-tree and assigned the quantities associated with the unique material
entity in the sub-tree.

#### Gold-related Target Refinement

With all relevant information from synthesis paragraphs extracted and
codified, we implemented a final target refinement step to identify all of
those papers that contain synthesis procedures explicitly targeting “gold” or
gold nanoparticle-related entities, a list for which is provided in the /rsc
folder of the GitHub codebase (see “Code Availability”). Of the 18,101
articles collected from the binary classification step described above, 5,154
contained a target entity extracted by MER (see “Synthesis Recipe Extraction”)
that was related to gold or a gold nanoparticle-related entity.

#### Tagging Seed-mediated Growths

Each paragraph containing a recipe was tagged as being related to either a
seed-mediated or seedless synthesis approach. Seed-mediated approaches are
those in which some method is used to create small colloidal seeds that act as
nucleation sites for larger growths, often with interesting morphology. These
methods are common for rod-based nanoparticle growths and are abundant in the
literature [39], so we wanted to make those particular recipes easily
queryable. The tags for this field were determined by keyword matching for
“seed” and related lemmas for seed-mediated methods as well as “seedless” or
the absence of seed-related text for seedless methods. The binary tag for
seed-mediated growth in a given recipe paragraph is included in the
seed_mediated field as a boolean in the provided dataset (see Table 2).

### AuNP Synthesis Outcome Extraction

To complement the extracted gold nanoparticle synthesis protocols from the
collected 5,154 articles, we also extracted relevant morphological and
characterization information. A two-step process was implemented for this
extraction.

#### Characterization Paragraph Classification

To focus on paragraphs that contain information on the morphology of
synthesized gold nanoparticles, we trained a binary transformer-based gold
nanoparticle characterization paragraph classifier, using a similar approach
to the binary synthesis paragraph classifier described above. Positive
training paragraphs were collected by manually selecting characterization-
related and morphology-related paragraphs modeled from LDA (with topic words
including “morphology”, “tem”, “size”, “diameter”, “nm”, etc.). The training
data for this classifier included 299 training examples, with 69 positive
examples and 230 negative examples. Using an 80/10/10 train/validation/test
split, a model was trained over the course of 20 epochs using Simple
Transformer’s ClassificationModel Module. This classification yielded 12,519
paragraphs containing morphological characterization information for gold
nanoparticles.

#### Characterization Entity Recognition (MorphER)

To extract relevant gold nanoparticle morphological information, we developed
a transformer-based named entity recognition (NER) model specializing in the
recognition of entities related to nanoparticle morphology and size
(“MorphER”). To train this model, we annotated a set of 119 characterization-
classified paragraphs from 91 articles on gold nanoparticle synthesis. The
entities labeled for this model include specific morphological information for
the synthesized gold nanoparticles, including: MOR, noun phrases related to
morphology, such as “nanoparticles” or “AuNRs”; DES, descriptive terms for
morphologies, such as “dumbbell-like” or “spherical”; MES, measurements, such
as “aspect ratio” or “diameter”; SIZ, the value of the measurement; UNT, unit,
if applicable (i.e. not for aspect ratios). Entities related to nanoparticles
(e.g. “NP”, “anoparticle”) but not necessarily their shape were labeled as MOR
entities since the shape of the particle is not always mentioned explicitly,
though the size is usually mentioned. This way, one could attribute extracted
size information to at least some target entity. We chose to use NER to
extract size information as well to deal with cases where we cannot use units
as an anchor for rule-based methods, as in aspect ratios for nanorods. The
model was fined-tuned over the pretrained MatBERT model described earlier (see
“AuNP Synthesis Paragraph Classification”) on the paragraph-level with an
80/10/10 train/validation/test split over 20 epochs and deep fine-tuning. This
entity recognition model was run on any paragraph that the AuNP synthesis
paragraph classifier predicted to be a synthesis paragraph or that the
characterization paragraph classifier predicted to be a characterization
paragraph.

This is the final extraction step in the pipeline constructed to build this
dataset. Thus, the dataset does not contain any entity linking (e.g. particle
size to specific nanoparticle morphology, morphological entity to synthesis
procedure, etc.). In attempts to address this for the next iteration of the
dataset, we have implemented, with moderate success, both rule-based linking
through dependency tree parsing as well as simultaneous extraction and linking
using more powerful language models such as GPT-3.

Briefly, we address our decisions for and differences in model choice and
architecture for the text entity extraction tools described above. Materials
Entity Recognition [33] and the synthesis actions extraction model were first
trained for the extraction of inorganic solid-state synthesis procedures [28].
The development of our MatBERT model (described in “AuNP Synthesis Paragraph
Classification”) was more recent and coincided with the development of the
Characterization Entity Recognition model. Since our development of MatBERT,
we have incorporated its embeddings into the Materials Entity Recognition
model since this tool is used on paragraphs outside of synthesis paragraphs.
Because the extraction task for synthesis actions is linguistically simpler
than for materials’ names, we continue to use the Word2Vec embeddings trained
for synthesis action extraction. Using MatBERT is also significantly more time
consuming (as determined by He et al. [33]) and Word2Vec embeddings are
sufficient for modeling word similarity. Additionally, the RNN model used for
synthesis action extraction is capable of capturing contextual differences for
certain vocabulary.

## Data Records

The dataset, with 7,608 synthesis paragraphs and 12,519 characterization
paragraphs from 5,154 articles, is provided as a JSON file, available publicly
at https://doi.org/10.6084/m9.figshare.16614262.v3 [40]. Each record
corresponds to a publication, represented as a JSON object in a top-level
list. Within each record is a list of paragraphs, with some containing a
codified recipe, extracted morphological information, both, or neither.
Metadata contained in the dataset for an article include: article DOI, the
year of publication, and the number of times the article has been cited as of
August 2021. For each paragraph within an article, metadata include: a unique
paragraph hash, a boolean indicating whether or not the paragraph contains
synthesis, a boolean indicating whether or not the paragraph contains
characterization information, a boolean indicating whether or not the
paragraph contains a seed-mediated growth, and a snippet of the paragraph
text. Expanded details for the format of the dataset are given in Tables 1, 2,
and 3.

## Technical Validation

The quality and content of this dataset is evaluated below through a
description of the data extraction model metrics as well as a comparison of
the dataset demographics to established heuristics in the field.

### Extraction Accuracy

We use the 35k article nanomaterial dataset developed by Hiszpanski et al.
[20] as a benchmark with which to compare our regex/tf-idf article filtering.
We note that this dataset is comprised only of publications from Elsevier, so
we only evaluate model performance on this set, which comprises 69% of our
total materials science literature collection. The 35k article gold standard
nanomaterial dataset contains 10,229 articles predominantly related to gold,
of which 2,577 are not contained in our original MongoDB collection and 602
were not captured by our tf-idf method. Inspection showed that the volume of
articles not contained in our database is largely due to our journal selection
during the scraping and parsing of articles, which focuses on materials
science-specific journals (whereas Hiszpanski et al. selected from all
publications in Elsevier journals). For the 602 articles not captured by our
data filtering, it was found on manual inspection that many only mentioned
“nano ” or “gold”-related vocabulary once or twice throughout the article or
only in the abstract. Such articles are not considered valuable for this
dataset since they likely do not contain recipes for gold nanoparticle
synthesis, so their absence is appropriate. No false positives (i.e. articles
that our pipeline determined to be related to gold nanomaterials but that
Hiszpanski et al. determined to be related to another composition) were found
from our extraction.

Manual validation was previously performed for 100 solution-based synthesis
paragraphs for another recently accepted dataset manuscript [41]. This was
done to determine the extraction accuracy of the rule-based methods used in
the extraction pipeline, which included synthesis action conditions (time and
temperature) as well as the amounts of materials used. These metrics are
included for reference in Table 4 as well. We accepted scores with higher
precision than recall for these rule-based methods in order to avoid
contaminating the dataset with incorrect information, though potentially
sacrificing completeness of a given codified recipe.

Manual checks on the validity of the seed-mediated growth tag for 50
paragraphs were performed, including 25 on paragraphs determined to contain a
seed-mediated growth method and 25 on paragraphs containing seedless growth.
49 out of the 50 checks were determined to be valid and true. 1 paragraph was
labeled as “seedless”, though it only contained purchasing information. We
still considered this tagging valid since the incorrect classification is due
to the synthesis paragraph classifier earlier in the pipeline. The accuracy
for this tagging method is shown in 4.

Finally, the F1 score, precision, and recall for each of the paragraph
classifiers and the MorphER model (along with the F1 score, precision, and
recall for each of the constituent entities) are also shown in Table 4. For
the binary classification models, similarly to the rule-based methods
discussed above, we accepted scores with higher precision than recall in order
to avoid erroneous classifications of paragraphs that should be data-rich, and
thus avoiding inflating the breadth of the present dataset.

### Dataset Mining

The statistical breakdown of the recipes contained in this dataset are
visualized and some basic correlations are explored. These are then discussed
in the context of current knowledge in the field.

First, we present an overview and statistical breakdown of the precursors used
for gold nanoparticle synthesis across the literature in Figure 2. Some
measures were first taken to standardize the information extracted by MER (see
“Synthesis Recipe Extraction”) from each of these paragraphs. This included
manually normalizing all synonyms for a given precursor to a single precursor
name, as well as investigating and mapping variances of their token
representations (e.g. from text-scraping errors, similar unicode characters,
typos, etc.) to a single precursor name. The map consists of appropriately
curated regex strings capturing these variations for a given precursor. This
mapping is provided as a JSON file in the associated GitHub repository for
this dataset (see “Code Availability”). The presence of each precursor among
seed-mediated and seedless growths is also reflected in this breakdown. The
overwhelming presence of $\textrm{HAuCl}_{4}$ is expected since this is the
most prevalent gold source for synthesizing nanoparticles, with
$\textrm{AuCl}_{3}$ and $\textrm{NaAuCl}_{4}$ following. 20 synthesis
paragraphs were inspected that did not show any of these gold sources
extracted as precursors. From these, it appeared that 15 paragraphs contained
incomplete synthesis descriptions in the text. These were most often brief
statements regarding the method of synthesis (e.g. “AuNPs were synthesized
through the Turkevich method…”) followed by a description of their resultant
size. Although the synthesis information for these paragraphs was incomplete,
they still often included successfully extracted morphological information
that we consider valuable for the purposes of this dataset. The remaining 5
paragraphs showed issues with materials entity parsing (see “Synthesis Recipe
Extraction”) due to unusual syntactic structure (here, the gold precursor
would be extracted and classified as _other_material_ as opposed to
_precursor_ , according to the data structure in Table 2). To better organize
the distribution of precursors, we binned each according to their function in
a given synthesis. Citrates were given their own bin since they can be used as
either a ligand or both a reducing agent and ligand (as in Turkevich [42] or
Frens [43] reduction), and because it is currently difficult to extract the
specific role of a precursor using our language processing tools. Strong and
weak reducing agents were binned together, where $\textrm{NaBH}_{4}$ is used
as a strong reducing agent while the other three are considered weak. This
breakdown also indicates which precursors are frequently used for seed-
mediated growths, like CTAB and $\textrm{AgNO}_{3}$ for the growth of nanorods
[44]. The common precursors used for seedless growths are often based on
attested reduction methods like Turkevich or Frens reduction in this dataset,
which both incorporate citrate-based precursors [45]. The lower frequency for
several of the precursors is likely due to their relatively recent
introduction into the field, such as PVP which was used first in gold
nanoparticle synthesis in 2017 to limit growth of nanorods as a capping agent
[4]. The low presence of water is likely due to the manner in which precursors
are extracted for this dataset using MER, which can extract precursor entities
like “water” and “$\textrm{H}_{2}\textrm{O}$”, but cannot infer water as a
precursor from descriptions of solutions like “aqueous”.

Moving beyond synthesis details, we also analyze the breakdown of the
morphologies discussed in the literature and how those have varied
cumulatively across time. Figure 3 represents the proportion of the most
discussed morphologies in the gold nanoparticle literature published between
1998 and 2021. For the purposes of this breakdown, only articles that discuss
a single morphology are considered. Through this filtration, the breakdown
consists of 1,744 articles out of the 5,154 in the dataset. Morphologies were
determined using the morphologies and descriptors fields in the
morphological_information field, which combines multiple-entity strings from
the MorphER extraction results. The synonym map used to normalize the
extracted entities is available in the associated codebase, which was
constructed in a similar manner to the precursors synonym map. The strong
presence of spherical particles across all years is due to their longevity in
the field, being synthesized through a formal procedure by Faraday as early as
1857 [46]. Spherical particles are also straightforward to synthesize, with
facile methods being pioneered by Turkevich [42] and Frens [43] in the 1950s
and 1970s, respectively. Rods are discussed in a quarter of collected
publications, more than any of the other anisotropic shapes combined. This
reflects the trends in the literature [9], mostly due to their highly tuneable
optical properties and more recently developed convenient wet synthesis
methods [47].

Finally, we explore correlations between the use of certain precursors and the
target morphology of a given synthesis. Using the filtration process described
above to consider only single morphology publications related to spheres,
rods, tubes, cubes, wires, and stars yielded 1,647 publications. Assuming the
one mentioned morphology is indeed the target, we developed a heat map
presenting the proportion of select precursors and common precursor ions
($\textrm{AuCl}_{4}^{-}$, citrate, CTAB, $\textrm{BH}_{4}^{-}$, ascorbic acid,
and $\textrm{Ag}^{+}$) mentioned in publications with each target morphology
(Figure 4). In this plot, “Citrate” also contains sodium citrate precursors.
The extracted precursors used in a given synthesis were matched against the
precursor synonym bank discussed previously. With this additional filtration
step, a total of 1,511 publications with only one of the select set of
morphologies mentioned and also having at least one of the select precursors
are shown in the heat map. A few general trends are reflected in this
illustration. First, the frequent mentions of CTAB, $\textrm{Ag}^{+}$,
ascorbic acid, and $\textrm{BH}_{4}^{-}$ are distinct for nanorod synthesis
publications. In particular, the use of $\textrm{AgNO}_{3}$ and CTAB to
control the quality and characteristics of gold nanorod growth is well-known
to the nanoparticle synthesis field [44]. $\textrm{AgNO}_{3}$ is used to
control the aspect ratios of the rods and there was a recent shift in seed-
mediated growth from citrate-capped gold seeds to CTAB-capped gold seeds
because the latter showed an improvement on earlier particle formation
limitations (e.g. noncylindrical rods, spherical impurities, etc.). Second,
citrate is most prominantly used in the synthesis of spherical particles. As
was discussed regarding the precursor breakdown earlier (Figure 2), citrate
was used in the seminal experimental works by Turkevich [42] and later by
Frens [43] as both a reducing and stabilizing agent. These methods are still
among the most prominent for synthesizing spherical gold nanoparticles, as is
reflected by Figure 4.

As was discussed in “Synthesis Recipe Extraction”, the language processing
tools and methods used to create this dataset were adapted from tools
previously developed for the extraction of solid-state [28] and solution-based
[41] recipes. The experimental methods used for nanoparticle synthesis are
distinct from other materials synthesis methods. This is particularly so from
solid-state methods, but even holds for other solution-based synthesis
methods. Because of this, we built additional extraction methods (see “AuNP
Synthesis Paragraph Classification”, “Characterization Paragraph
Classification”, and “Characterization Entity Recognition (MorphER)”), on top
of those that were used for the construction of text-mined solid-state and
solution-based recipe datasets, to better handle such synthesis details.
However, there are still some pitfalls in this combination of extraction
methods that we are addressing for future iterations of this dataset. First,
the order of synthesis actions is particularly important in seed-mediated
nanoparticle synthesis, which is found to represent a substantial fraction of
the major synthesis methods found in the literature (see Figure 2). This
method is comprised of a seed solution preparation step, a growth solution
preparation step, and a step that combines these two. Currently, our synthesis
action extraction method cannot distinguish these three steps as separate
synthesis procedures. Therefore, isolating specific synthesis procedures for
the components of a given seed-mediated synthesis is difficult. Because the
seed and growth solutions are often described as ingredients in the text, they
can be captured in the subject field of the procedure_graph (Table 3), which
is determined through dependency tree parsing. To address this issue, noun-
phrases parsed in the subject field can be used to define the relevant
synthesis constituent being manipulated or prepared, and thus separate the
synthesis procedures into components for seed-mediated growth. Second, the
current materials entity recognition model does not detect entities that do
not contain specific material formulae or chemical names. Thus, neither “AuNP
seed solution” nor “growth solution” would be detected in the sentence “…3 mL
of AuNP seed solution was mixed with 5 mL of growth solution to produce the
final nanorods.” Because of this, the corresponding amounts for each component
of the synthesis cannot be extracted. Such information is important for seed-
mediated growth, so we plan to address this by using the results of the
aforementioned subject field and use seed and growth solution-related noun
phrases as anchors for an additional material amounts extraction step if the
paragraph describes seed-mediated synthesis. Finally, there is currently no
way to distinguish extracted morphologies as either the desired target
morphology or just a morphology mentioned off-hand by the author. To address
this, we plan to develop an additional layer on top of the current morphology
entity recognition model that classifies those entities predicted to be MOR
into either target (TGT) or miscellaneous morphologies (MIS), similar to the
strategy used for materials entity recognition (see “Synthesis Recipe
Extraction”).

## Usage Notes

The present dataset is provided as a single JSON file that can be read using
all major programming languages (e.g. Python, Matlab, R, etc.). It is publicly
available at https://doi.org/10.6084/m9.figshare.16614262.v3 [40]. No
dependencies are required to access the contents of the dataset.

We invite users to utilize this dataset, among other applications, for the
purposes of gold nanoparticle synthesis literature reviews or to query
specific recipe protocols that achieve a desired morphology or size.

This data descriptor defines a static version of the gold nanoparticle
synthesis and characterization dataset; however, we intend to update the
dataset in the repository below on a regular basis here:
https://github.com/CederGroupHub/text-mined-aunp-synthesis_public. This will
soon include updates addressing the issues discussed at the end of “Dataset
Mining” as well as for morphological entity linking and linking target
morphologies to specific recipe protocols.

## Code Availability

Scripts developed for the generation of this dataset as well as notebooks for
example data analysis are available at https://github.com/CederGroupHub/text-
mined-aunp-synthesis_public, along with an acknowledgement for this paper. The
libraries use for this project are: _ChemDataExtractor_ , _SpaCy_ , _scikit-
learn_ , _gensim_ , _Tensorflow_ , _Keras_ , _PyTorch_ , and _Simple
Transformers_.

## Acknowledgements

This work was funded by the U.S. Department of Energy, Office of Science,
Office of Basic Energy Sciences, Materials Sciences and Engineering Division
under Contract No. DE-AC02-05-CH11231 (D2S2 program KCD2S2). We thank Anna
Sackmann, Rachael Samberg, and Timothy Vollmer (Science Data and Engineering
Librarians at UC Berkeley) for assistance in obtaining Text and Data Mining
agreements with the relevant publishers. We would also like to thank Sam
Gleason, Xingzhi Wang, Jakob Dahl, Caitlin McCandler, John Dagdelen, Nicholas
Walker, and Akshay Subramanian for valuable advice and discussion regarding
the development of this pipeline and analysis of the data.

## Author Contributions

K.C. developed the extraction pipeline, annotated paragraphs for and trained
the relevant paragraph classifiers and MorphER model, analyzed the data, and
wrote the manuscript. A.T. provided guidance for approaches in developing the
pipeline and developed framework for MorphER training. S.L. performed manual
data inspection, advised on updates to extraction methods, and analyzed the
data. Z.W. developed the material quantities extraction method. H.H. developed
the Apache Solr search engine and trained MatBERT. T.H. developed materials
entity recognition. O.K. developed synthesis action and conditions extraction
method. A.J. supervised the project and wrote the manuscript. G.C. developed
the approach, supervised the project, and wrote the manuscript. All authors
contributed to the final manuscript.

## Competing Interests

The authors declare no competing interests.

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## Tables

[b]  Data description Data Key Label Data Type DOI of the original paper doi
_string_ List of constituent paragraphs and extracted data paragraphs _list_
of Objects (_dict_)1 Year of publication publication_year _int_ Number of
citations times_referenced _int_

  * 1

Contents of paragraphs shown in Table 2

Table 1:  Format for highest article-level of each data record: description,
key label, data type.

[b]  Data description Data Key Label Data Type Unique paragraph hash _id
_string_ Whether paragraph contains characterization information
contains_characterization _bool_ Whether paragraph contains synthesis recipe
contains_recipe _bool_ Materials and quantities contained in paragraph1
materials_and_quantities _list_ of Objects (_dict_): -material: _string_
-amount: _list_ of Objects: \- -value: _float_ \- -unit: _string_ Condensed
morphological entities in paragraph2 morphological_information Object
(_dict_): \- descriptors: _list_ \- measurements: _list_ \- morphologies:
_list_ \- seeds: _list_ \- sizes: _list_ \- units: _list_ Morphological
entities and token locations in paragraph2 morphology_ner_tokens _list_ of
Objects (_dict_): \- annotation: _string_ \- start: _int_ \- end: _int_ \-
text: _string_ Whether or not the AuNP synthesis is seed-mediated1
seed_mediated _bool_ List of constituent sentences and extracted data1
sentences _list_ of Objects (_dict_)3 Synthesis actions and conditions1
synth_actions _list_ of Objects (_dict_): -conditions: Object (_dict_): \-
-temperature: Object(_dict_)4 \- -time: Object(_dict_)4 -string: _string_
-subject: _string_ -type: _string_ Snippet of paragraph text text _string_

  * 1

Only if contains_recipe is true

  * 2

Only if contains_characterization or contains_recipe is true

  * 3

Contents of paragraphs shown in Table 3

  * 4

{value: _list_ , unit: _string_ , max_value: _float_ , min_value: _float_}

Table 2:  Format for lower paragraph-level of each data record: description,
key label, data type.

[b]  Data description Data Key Label Data Type All material entities
all_materials _list_ of Objects (_dict_)1 Non-precursors and non-target
material entities other_materials _list_ Precursor material entities
precursors _list_ of Objects (_dict_)1 Sequence of synthesis operations and
conditions procedure_graph _list_ of Objects (_dict_) \- env_toks: _list_ \-
op_token: _string_ \- op_type: _string_ \- ref_op: _bool_ \- subject: _string_
\- temp_values: _list_ of Objects (_dict_)2 \- time_values: _list_ of Objects
(_dict_)2 Target material entities target _list_ of Objects (_dict_)1

  * 1

{material: _string_ , amount: [{value: _float_ , unit: _string_}]}

  * 2

{max: _float_ , min: _float_ , tok_ids: _list_ , units: _string_ , values:
_list_}

Table 3:  Format for lowest sentence-level of each data record: description,
key label, data type.

[b]  Pipeline Component ML Method F1: (precision|recall) Article filtering
regex/tf-idf 0.96: (1.00|0.92) Synthesis paragraph classification BERT
classification 0.90: (0.96|0.85) Characterization paragraph classification
BERT classification 0.90: (0.93|0.87) Materials Entity Recognition BiLSTM+CRF
(MatBERT embeddings) 0.95:(0.95|0.95) - materials1 0.90:(0.89|0.91) -
precursors1 0.85:(0.86|0.83) - targets1 Morphology Entity Recognition Fine-
tuned MatBERT NER model 0.87:(0.89|0.84) - Micro average 0.92:(0.90|0.95) -
MOR (morphology) 0.56:(0.70|0.52) - DES (descriptor) 0.70:(0.83|0.64) - MES
(measurement) 0.69:(0.81|0.62) - SIZ (size value) 0.91:(0.94|0.91) - UNT
(unit) Synthesis actions2 BiLSTM (Word2Vec embeddings) 0.89 (0.90|0.88)
Synthesis conditions3 Rule-based \- Temperature 0.94: (0.97|0.92) \- Time
0.93: (0.98|0.89) Material quantities3 Rule-based 0.87: (0.90|0.85) Seed-
mediated tag Rule-based 1.00: (1.00|1.00)

  * 1

Metrics from He et al. [33]

  * 2

Metrics from associated manuscript on synthesis actions extraction [48]

  * 3

Metrics from accepted publication on solution synthesis extraction [41]

Table 4:  Text extraction model accuracies

## Figures

Figure 1:  AuNP synthesis publication extraction pipeline. Starting with the
>4.5 million article materials science literature database, parsed paragraphs
from the articles are funneled through progressively finer-meshed filters to
identify those related to the synthesis of gold nanoparticles. The first two
steps include a regex search for "nano " phrases followed by the vectorization
of that corpus using tf-idf, similar to the method used in Hiszpanski et al.
[20]. Those articles where the tf-idf scores for gold are higher than other
noble metal nanoparticle compositions are accepted. Each paragraph from those
articles is then passed through a binary classifier which determines whether
or not the paragraph describes the synthesis of gold nanoparticles. Finally,
after extracting the synthesis recipes from those relevant paragraphs, 5,154
articles with synthesis paragraphs containing gold- or gold nanoparticle-
related targets are collected. An example synthesis paragraph along with a
sample of the extracted information is shown in the bottom panel.  Figure 2:
Frequencies of most common AuNP synthesis precursors. The most frequently
extracted precursors using materials entity recognition (MER, “Synthesis
Recipe Extraction”) were inspected and compiled into a regular expression-
based synonym map, which is housed in the available code repository (see “Code
Availability”). The precursors are binned by their function in AuNP synthesis
and their presence in the number of publications employing seed-mediated
growth or seedless methods is distinguished. Citrates are considered in their
own category since their function varies depending on the method used, for
instance citrates are used as a reducing agent and a ligand in Turkevich or
Frens reduction, but only as a ligand in other reduction methods. Only the
precursors appearing in more than 50 articles are shown. Precursors were
counted once per article within the seed-mediated and seedless growth
categories for this analysis to avoid double counting precursors which may be
mentioned in a purchasing paragraph and a synthesis paragraph, both of which
can sometimes be classified as a synthesis paragraph by our binary gold
nanoparticle synthesis paragraph classifier.  Figure 3:  Breakdown of reported
AuNP morphologies discussed by year. Similarly to the frequent precursors
analysis, we compiled a regular expression-based synonym map for the most
frequently extracted morphological entities and descriptors. The timeline
represents the cumulative number of publications discussing each of the
specified morphologies from 1998-2021. The entire plot represents 1,744
articles from the dataset, each of which discuss only one of the morphologies
in the set depicted.  Figure 4:  Heatmap depicting correlation between
precursors and resultant AuNP morphologies. The heat illustrated in a given
cell represents the fraction of morphologically-targeted articles (say, the
fraction of sphere-related articles) which use that particular precursor among
one or more precursors it uses in the recipe. For instance, the top left cell
shows that more than 90% of purely sphere-related AuNP synthesis papers use
$\textrm{AuCl}_{4}^{-}$ as a precursor. "Citrate" also includes sodium citrate
precursors. The entire heatmap describes 1,511 single morphology-targeted
articles with at least one of the precursors or precursor ions shown on
y-axis.