Advanced Intelligent Systems - 2020 - Sha - Artificial Intelligence to Power the Future of Materials Science and.pdf

Artificial Intelligence to Power the Future of Materials
Science and Engineering
Wuxin Sha, Yaqing Guo, Qing Yuan, Shun Tang, Xinfang Zhang, Songfeng Lu,
Xin Guo, Yuan-Cheng Cao,* and Shijie Cheng
1. The Merging of Materials Science and Artificial
Intelligence
From the Paleolithic Age to the coming fourth industrial revolu-
tion, the millions of years of human history is mainly marked by
materials. Material science is mainly to explore the relationship
between materials structure, process,
properties, and application. The discovery
of new materials will play a greater role
in promoting the development of human
society. After several centuries of develop-
ment, a large amount of data has been accu-
mulated in the field of materials science.[1]
However, the inherent limitations of
human cognitive ability make it difficult
for human beings to absorb and process
the massive literature and data produced
every day.[2]
Only a small part of data
(compared with the whole data volume)
can be analyzed in a certain subdivision
field. The current material research is
mainly a “trial-and-error method” based
on a large number of experiments guided
by experience, and a small number of
computer simulation calculation as a
supplement, which consumes a lot of
manpower, time, materials, and financial resources.[3]
The vast
amount of material information data are always silent in the
database or used little by little. Therefore, finding a new research
method is necessary to accelerate material innovation.[4]
The emergence of artificial intelligence (AI) brings a new
dawn to the development of material science.[5]
After more than
60 years of development, from the simple perceptron[6]
to com-
plex multilayer neural networks,[7]
AI has exhibited a primary
algorithm framework and a powerful hardware foundation.[8–13]
Some advanced AI system even defeated world champions in
many domains, such as Chess,[14]
Go,[15]
quiz game,[16]
and other
fields.[17–23]
The excellent data mining ability of AI has attracted
the wide attention of the material science community.[24–27]
Jim Gray, the winner of Turing Award, proposed “the fourth
paradigm of science” at the NRC-CSTB conference[28]
in 2007.
It is a data-intensive science that combines big data and AI to
compress lots of known information into unknown theories to
guide scientific innovation.[29]
This method is suitable for dealing
with large-scale composite space or nonlinear processes, which
reminds some problems in material research. Materials infor-
matics, the combination of materials science and AI techniques,
is such an interdiscipline to help scientists to effectively obtain
the hidden relationship between different variables, predict the
specific properties of materials, guide the chemical synthesis
route, optimize the process parameters, and upgrade the existing
material characterization methods.
Machine learning (ML) is an important branch of AI which
develops rapidly in recent years, and it is also the most promising
W. Sha, Q. Yuan, Dr. X. Zhang, Dr. S. Lu
School of Computer Science and Technology
Huazhong University of Science and Technology
Wuhan 430074, China
W. Sha, Y. Guo, Dr. S. Tang, Prof. Y.-C. Cao, Prof. S. Cheng
State Key Laboratory of Advanced Electromagnetic Engineering and
Technology
School of Electrical and Electronic Engineering
Wuhan 430074, China
E-mail: yccao@hust.edu.cn
Prof. X. Guo
School of Materials Science and Engineering
Wuhan 430074, China
The ORCID identification number(s) for the author(s) of this article
can be found under https://doi.org/10.1002/aisy.201900143.
© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA,
Weinheim. This is an open access article under the terms of the Creative
Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
DOI: 10.1002/aisy.201900143
Artificial intelligence (AI) has received widespread attention over the last few
decades due to its potential to increase automation and accelerate productivity.
In recent years, a large number of training data, improved computing power, and
advanced deep learning algorithms are conducive to the wide application of AI,
including material research. The traditional trial-and-error method is ineffi-
cient and time-consuming to study materials. Therefore, AI, especially machine
learning, can accelerate the process by learning rules from datasets and building
models to predict. This is completely different from computational chemistry
where a computer is only a calculator, using hard-coded formulas provided by
human experts. Herein, the application of AI in material innovation is reviewed,
including material design, performance prediction, and synthesis. The realiza-
tion details of AI techniques and advantages over conventional methods are
emphasized in these applications. Finally, the future development direction of
AI is expounded from both algorithm and infrastructure aspects.
REVIEW
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Adv. Intell. Syst. 2020, 2, 1900143 1900143 (1 of 12) © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

application of AI in the research of material science. The next part
introduces the basic knowledge of ML, which lays the foundation
to introduce the materials research applications of AI in later text.
2. Basics of ML
ML describes a computer’s ability to train on a set of data and then
find the regulations or knowledge underlying that data. To be spe-
cific, ML is mainly divided into four steps: data collection, data
representation, algorithm selection, and model optimization.[30]
2.1. Data Collection
ML is a kind of data-driven algorithms, and data can be obtained
by simulations (such as density functional theory [DFT] and
molecular dynamics [MDs]), experiments, and online data-
base.[31]
Data include physical properties and structural informa-
tion on some materials. Many data in the field of materials are
missing, repeated, and inconsistent because of the limitation of
environment and experimental conditions. So, data cleaning, to
identify and correct different errors in original data, becomes fairly
necessary.[32]
For missing values, the average, minimum, or other
statistical values of the attribute are used to fill in the vacancy as
appropriate.[33–35]
For repeated values, the basic idea of eliminat-
ing duplicate records is sorting by attribute values and merging
records with identical value. The related algorithms include
priority queue algorithm, sorted-neighborhood method, and so on.
Such methods have been used in perovskite data by merging
different entries in the Materials Project database and the
Inorganic Crystal Structure Database.[36]
For inconsistent values,
according to the reasonable value range and mutual relationship of
each variable, specific programs can be designed to check whether
the data meet the requirements.[37]
Data beyond the normal
range or conflicting attributes will be deleted appropriately. After
cleaning, the data can be used for data representation.
2.2. Data Representation
Data representation is converting the raw data into some forms
suitable for an algorithm. The data we collect is usually numeric
but may not be appropriate for the algorithm. Just as when we
solve mathematical problems, we prefer to list equations or
plot-relevant figures to help us understand better. ML algorithms
also need an appropriate form of input data to learn better. The
more appropriate representation we use, the better the model
performs.
One of the methods to represent physical properties and struc-
tural information is binary coding. Granda et al. proposed an
organic synthesis robot.[38]
By binary coding the chemical input,
the robot can analyze the reactivity of reagent combinations, and
use support vector machine (SVM) model to predict unknown
chemical reactions.
2.3. Algorithm Selection
ML is generally classified into supervised learning (such as clas-
sification and regression) and unsupervised learning (such as
clustering), depending on whether the training data are labeled
or not. Due to the recent improvement in materials automation,
reinforcement learning and active learning, which need to inter-
act with the environment, are also emerging in the application of
materials research. Currently, the most popular algorithms
include k-nearest neighbor (KNN), decision tree symbolic regres-
sion, and artificial neural networks. A brief introduction about
these methods will be provided in the following sections.
KNN is a classification and regression algorithm, which is very
simple and effective.[39]
Given a training dataset and a new
datum, the algorithm finds k entries in a dataset that are nearest
with the new datum, and the new datum will be classified in the
category which appears most frequently. The algorithm consists
of the selection of k, distance measurement, and the rule of
classification. The model complexity degree increasing with
Wuxin Sha received his bachelor’s
degree in the School of Materials
Science and Engineering from
Huazhong University of Science and
Technology (HUST) in 2017. He is
currently pursuing his Ph.D. degree in
the School of Computer Science and
Technology, HUST. His research
interests focus on AI-assisted
materials genome, ML, and solid-state
electrolytes lithium batteries.
Yuan-Cheng Cao is currently a
professor of the State Key Laboratory
of Advanced Electromagnetic
Engineering and Technology at
Technology (HUST, Wuhan). He
received his Ph.D. degree from HUST
in 2006. Then he worked at
Nottingham Trent University (UK,
2007–2010), Newcastle University (UK,
2010–2014), and Jianghan University (Wuhan, 2014–2018).
His current research interests include solid-state
electrolytes in energy-storage batteries, safety and
extinguishing control for grid energy storage, eco-friendly
recycling, and regeneration of decommissioned batteries.
Shijie Cheng is a professor of
Technology. He received his
bachelor’s degree from Xi’an Jiaotong
University in 1967, his master’s
degree from HUST in 1981, and his
Ph.D. degree from the University of
Calgary (Canada) in 1986, respectively,
all in electrical engineering. In 2007,
he was elected as a member of the
Chinese Academy of Sciences. He is currently engaged in
research on energy-storage systems for electric power
system stability and advanced materials for electrical
engineering.
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the k becomes smaller, the approximation error will decrease,
and the estimation error will increase. Using different distance
to measure similarity with two points may lead to different
results. KNN can select Euclidean distance, Manhattan distance,
and so on. KNN usually selects majority voting as a rule of
classification, because it means empirical error minimization.
Decision tree is one of the simplest and most successful
algorithms in ML.[40]
A decision tree represents a classifier which
takes a series of attribute values as input and outputs a decision.
The input and output values can be either discrete or continuous.
If the inputs are discrete and output only has two possible
values, it is called Boolean classification. A decision tree
outputs its decision by performing a set of tests. In decision
trees, each node represents a test of the value of one of the input
attributes, and the branches from it are possible values of the
attribute. Each leaf node is a value which is returned by
the function.
Symbolic regression, especially genetic programming-based
symbolic regression (GPSR), is a classical AI algorithm.[41]
It is different from the traditional numerical regression because
the functional relationship between variables is not given.
Instead, the functional form is gained by the evolution of chro-
mosomes in each candidate function. The chromosomes consist
of a set of internal nodes with mathematical operation symbols
and terminal nodes with variables and constants. The depth-first
search algorithm can be used to traverse chromosomes to obtain
the corresponding function. The error between the experimental
data and the fitted data by the function is used as the evaluation
function. The candidate functions with the smallest error, and
the largest adaptability could create descendants preferentially.
Different chromosomes pass through mutation and heredity,
and gradually iterate until the best form of function and parame-
ter set for a given problem is found.[42]
GPSR is suitable for the
field of material research with little prior knowledge and unclear
relationship between related variables, such as the magic angle in
graphene,[43]
the viscosity of normal hydrogen,[44]
and the search
for descriptors of perovskite stability.[45]
Inspired by the hypothesis that mental activity primarily con-
sists of electrochemical activity in networks of brain cells called
neurons, artificial neural networks are created. Neural network
consists of nodes connected by directed links. Each link between
nodes serves to propagate activation and has a numeric weight
associated with it, which determines the strength and sign of
the connection. There are two basic ways to connect nodes to
form a network. If nodes are connected in one direction, the
network is a feed-forward network. If a network feeds its outputs
back into its inputs, it is a recurrent network. The most com-
monly used network consists of more than three layers, includ-
ing the input layer, the output layer, and hidden layers. The
learning process is to find appropriate parameters to minimize
the output error rate. After training and testing strategy, the
model is well-established.
There are more efficient ML algorithms in addition to that
mentioned earlier, such as random forests, kernel methods, con-
volutional neural networks, and generative adversarial networks
(GAN). Whatever algorithm selected, there are some hyperpara-
meters to be estimated by human or other heuristic algorithms.
Recently, there are more researches in automatic ML, which
aims to make it easier for people to apply ML algorithms.
2.4. Model Optimization
The model which has higher-degree polynomials can fit the train-
ing data better, but it will overfit and perform poorly on validation
data if the degree is too high. There are two ways to choose the
degree of the polynomial: cross-validation and regularization to
directly minimize the weighted sum of the empirical loss and the
complexity of the model.
To search for a model with as low as possible error rate, loss
function is usually used. The loss function is defined to measure
the distance of correct values and predicted values. By minimizing
the loss function, the best hypothesis can be found. Cross-
validation is reliable only when the samples used for training
and validation are representative of the whole population.
3. AI Applications for Materials Science and
Engineering
In recent years, AI has been applied in more and more fields, and
ML research in the field of materials is rapidly developing,
especially in that it can synthesize new materials and predict
various chemical synthesis.[46,47]
In this section, we will explore
how ML can help people solve the barriers between designing,
synthesizing, and processing materials.[48–54]
3.1. Accelerated Simulation
The research process for computational chemistry and materials
science has been updated to the third generation. The first gener-
ation refers to the calculation of “structure-performance”, which
mainly takes advantage of the local optimization algorithm to
predict the performance of the materials from the structure. The
second is “crystal structure prediction”, which mainly adopts
global optimization algorithm to predict structure and performance
from element composition. The third generation recognized
as “statistically driven design,” utilizes ML algorithms to predict
the composition, structure, and performance of elements from
physical and chemical data.[55,56]
However, the imperfection of
the theory has also brought obstacles to the discovery of high-
performance materials and the parameters of the model are not
completely consistent with the practical conditions such as mixed
phase or grain boundary. For example, the DFT prediction[57]
of
zirconium-doped lithium tantalum silicate is 10 3
S cm 1
, whereas
subsequent experiments have shown that its actual conductivity
is about 10 5
S cm 1
.[58]
Therefore, finding ways to use ML to
make up for the deficiencies of simulation is very important.[59,60]
3.1.1. Atom2vec
Atom2Vec, an unsupervised ML program, reconstructed the
periodic table of elements only in a few hours. Atom2Vec first
learns to distinguish different atoms by analyzing the list of
compounds in the online database. Then, we borrow the simple
concept of natural language processing: the characteristics of a
word can be derived from other words around it; chemical
elements are clustered according to their chemical environment.
At the same time, the vectorized atomic descriptor can be used as

the input of many ML models because it carries a large amount of
information about the periodic law of elements, which provides
an effective new way for the quantitative representation of mate-
rial data in the future.[61]
3.1.2. Increasing Simulation Scale
Because there are some regular repetitions in the theoretical
calculation of atomic force field, once ML finds these repetitive
patterns, the corresponding energy or force field can be calcu-
lated quickly. The movement of hundreds of atoms in a few pico-
seconds can be enlarged to that of millions of atoms in a few
nanoseconds, which greatly increases the length and time range
of the simulation calculation, and achieves better results.
Complex material structures (such as amorphous, polycrystal-
line) and chemical reactions (corrosion, interfacial reactions,
etc.) might be simulated.
In large-scale MDs, simulations of surface and interfacial
chemical processes, the development of reliable interatomic
potentials is a formidable challenge because of the existence
of a wide range of atomic environments and very different types
of bonds. In recent years, the interatomic potential based on arti-
ficial neural networks (NNs) has emerged, which provides an
unbiased method for the construction of potential energy surface
of systems that are difficult to describe by traditional potential.
Artrith et al. used copper and zinc oxide as reference systems
to verify the accuracy and validity of the interatomic potential
of the artificial neural network and described the CuZnO
ternary combination system of oxide-supported copper clusters
(Figure 1).[62]
Generally speaking, the potential energy of the neu-
ral network is very precise with the results close to the calculation
value of the basic reference electronic structure and several
orders of magnitude higher efficiency. Compared with other
potential-energy calculation methods, the construction of NN
potential energy requires higher computational requirements
because of the need for a large number of training points. But
the advantages of NN in large-scale applications where traditional
electronic structure calculation is hard to solve are evident.
3.1.3. Reducing the Amount of Computation
Due to the massive combination spaces of materials, it is difficult
to explore all possible combinations in a reasonable time by
traditional simulation calculation. For example, the bimetallic
configuration of the smallest known sulfide nanocluster
Au15(SR)13 exceeds 32 000, and traversing all potential structures
is a huge computational challenge. However, if a small part of the
data is used to train the ML model, and then the model is used to
predict the other combinations, the computational complexity will
be greatly reduced and the filtering speed will be increased by
several orders of magnitude. Panapitiya et al. proposed a ML
model based on stochastic forest method to predict CO adsorption
energy of nanoclusters.[63]
First, the DFT simulation data training
model of Ag-alloyed Au25 nanoclusters was used. Using two-step
feature selection process and feature engineering method,
the authors predicted the adsorption energy with accuracies of
0.78 (R2) and 0.17 (RMSE). After interpreting the key nodes of
random forest, the authors found that the distribution of Ag
atoms in Au25 had the most important effect on CO adsorption
sites. The ML model can be easily extended to other nanoclusters
based on Au. The model is expected to be used as a screening tool
to screen eligible materials for further accurate analysis.
3.2. Predicting the Property of New Materials (Mapping
Structure-Property Relationship)
Material researchers generally hope that desired properties of
materials can be optimized, such as the conductivity of electro-
lytes, the Seebeck coefficient of thermoelectric materials, and the
power conversion efficiency of organic–inorganic hybrid perov-
skites.[64–66]
A large number of trial-and-error experiments based
on theoretical simulation or chemical scientists’ intuition typi-
cally lead to dissatisfactory results. Fortunately, the applications
of ML models can help a lot by predicting the properties and
structures of materials with an acceptable accuracy before synthe-
sis. Sendek et al. used the ML model developed in MATLAB to
find a small amount of special solid electrolytes in more than
12 000 materials.[67]
Using a well-known set of electrolytes and
their atomic structures for training, they first combed the scien-
tific literature and found 40 solid crystalline materials. Because of
the small size of the dataset, it is necessary to use the “intelligent”
feature based on existing physical knowledge for data represen-
tation. Therefore, the author downloads the atomic structure of
these 40 materials from ICSD as input, and calculates 20 kinds of
characteristics according to the atomic position, mass, electro-
negativity, and atomic radius of the structure, including the
volume of each atom, the lithium bond ionicity, the number
of lithium adjacent elements, and the minimum anion–anion
separation distance, and describes the atomic local arrangement
and chemical characteristics of each crystal. Then these 20 fea-
tures are used as inputs, the experimental values of lithium-ion
Figure 1. Schematic structure of a high-dimensional neural network
potentials for a system of the composition CuxZnyOz. For each atom i
in the system there is one line. Each circle on the left side represents
the Cartesian coordinate vector of an atom. These are then transformed
to symmetry function vectors Gi describing the local atomic environments.
The Gi are then used as input vectors for atomic NNs yielding the
atomic energy contributions Ei to the total energy E. Reproduced with
permission.[62]
Copyright 2013, Wiley.

conductivity are used as outputs, and 40 known materials
constitute the training set of a ML algorithm. After constant
parameter adjustment, the model can screen and classify solid
electrolytes. Then 317 candidate materials were predicted. The
results show that the efficiency of identifying potential new
materials using the modified MATLAB model is three times
higher than that of random guessing and two times higher
than that of Stanford graduate students working in related
fields. Compared with DFT results, the F1 score is about 50%
(Figure 2).
The training data of ML can be not only from experimental
tests but also from high-throughput simulations. Li et al. studied
the thermodynamic stability of double perovskite halides using
high-throughput calculation and ML.[68]
First, they established a
decomposition energy database based on high-throughput DFT,
which was closely related to the thermodynamic stability of 354
perovskite candidates. Based on this database, they trained a ML
model. The experimental observation of perovskite formability of
246 A2B(I)B(III)X6 compounds (F1 score, 95.9%) further verified
its prediction performance. This work shows that the ML model
prediction is more economical and effective than experimental
attempts.
Similar methods have been applied to the design of lead-free
organic–inorganic hybrid perovskite,[64]
monoatomic catalysts,[69]
light-emitting diode (LED),[70]
organic light-emitting diode
(OLED),[71]
and other key materials. The latter two methods have
also been verified by experiments. At present, material science is
not a complete trial-and-error method. Some theories are still
used to reduce the number of experiments, and the demand
for reduction will be higher and higher in the future. Or the
regression model can be used to select the material with the best
interesting performance from a large number of alternative
materials, which can effectively reduce the number of error
experiments in trial-and-error methods.
3.3. Synthetic Route Planning
Organic synthesis has a standard process that allows scientists to
design computer programs to deal with synthetic problems.[72]
As far as computer scientists are concerned, a chemical reaction
is a set of data that indicates the relationship or connection of a
compound. This presence can be expressed as a data structure,
such as a graph or network.[73,74]
Then AI could deal with these
structural data to guide the synthesis route.[75]
Granda et al. presented an organic synthesis robot that
includes online spectral analysis and feedback loop to perform
six experiments simultaneously.[38]
Its core components include
a raw-material tank and a pressure pump assembled with chem-
icals. These pumps are responsible for feeding reactants into six
parallel-operated reaction bottles. In addition, the robot uses the
SVM method to automatically classify the reaction mixture into a
reactive or nonreactive mixture by real-time evaluation of the
reaction using NMR and IR spectroscopy. This method is faster
than manual experiments and can predict the reactivity of
reagent combinations. Also, after collecting the results of about
10% of the experimental dataset, the robot could predict the reac-
tivity of 1000 reaction combinations with a prediction accuracy
of over 80% and discovered four new reactions (Figure 3).
In addition to data-driven methods, the researchers also used
reaction rules to predict retrosynthesis analytic systems and
developed logic-based and knowledge-based search strategies to
design the reaction route. Therefore, the proposed retrosynthesis
method can theoretically obtain a reasonable starting material
and a reaction route by analyzing the desired compound.
Nowadays, this technology has been applied to synthesize new
materials and predict various chemical syntheses.
The difficulty in retrosynthesis is finding ways to express the
existing chemical reaction in a data structure amenable to
algorithms. Schneider et al. proposed a new chemical reaction
fingerprint and classified the organic reaction into 50 models
(Figure 4).[76]
Combining with random forests, Naive Bayes,
K-means, and logistic regression methods, they can correctly
predict nearly 97% of organic synthesis. In the past 10 years, sci-
entists have used various rule-based algorithms to predict
organic reactions. Furthermore, scientists could take advantage
of ML to determine which rule the reaction should choose.
Segler et al. first collected about 12.5 million chemical reac-
tions published by 2014.[77]
Three different neural networks
Figure 2. Schematic of comparison between conventional DFT and machine learning approach. Reproduced with permission.[67]
Copyright 2018,
American Chemical Society.
Adv. Intell. Syst. 2020, 2, 1900143 1900143 (5 of 12) © 2020 The Authors. Published by WILEY-VCH Verlag GmbH Co. KGaA, Weinheim

are combined with Monte Carlo tree search (MCTS) to form a
new AI algorithm (3N-MCTS) to find the appropriate inverse syn-
thesis route. Three kinds of neural networks are applied to the
expansion and display of search nodes (Figure 5). Researchers
trained these networks using chemical reactions recorded in
the Reaxys database before 2015, validated and tested the models
using records published after 2015, and finally successfully
planned new chemical synthesis routes. In subsequent double-
blind experiments, 45 organic synthesizers try to choose synthetic
routes for nine complex molecules. 57% of the staff chose the
route of 3N-MCTS design and 43% chose the route of literature
report. This suggests that even authoritative synthetic chemists
find it difficult to distinguish between the software and human
chemists. Compared with the traditional synthesis methods,
more synthetic routes can be predicted in a shorter time using
the new AI technology. This research is a breakthrough in AI
applied for chemical synthesis. Mark Waller has also been hailed
as the pioneer of “chemical AlphaGo” by the media.
With the aid of simulation calculation and material informat-
ics, the design and performance prediction of new materials can
be completed. However, finding ways to predict the synthesis
method of these new materials is the bottleneck in the current
material research. Researchers usually need months or even
years of repeated trial-and-error experiments to get a mature syn-
thesis method of new compounds, and the corresponding exper-
imental parameters and results varying with the environment
will also bring difficulties for wider learning and application.
The establishment of material synthesis information database
is an important step to overcome this bottleneck.
Kim et al. collaborated to obtain synthetic conditions from
published literature using ML and natural language processing
techniques.[78]
AI platform developed by researchers can auto-
matically analyze literature, and classify them according to the
keywords mentioned in the text, such as synthesis temperature,
time, equipment name, preparation conditions, and target mate-
rials. The results show that the platform has 99% accuracy in
identifying passages and 86% accuracy in tagging keywords.
Using this platform, the researchers analyzed the synthesis
conditions of various metal oxides in 12 900 pieces of literature,
and successfully predicted the key parameters needed for hydro-
thermal synthesis of titanium dioxide nanotubes based on the
obtained data. This technology is an important progress in
the Material Genome Project. It is expected to greatly reduce
the difficulty in developing new materials and save the time
of developing new materials.
Subsequently, Huo et al. constructed a semi-supervised
ML method, which was used to obtain and classify inorganic
material synthesis information in batches from natural language
documents.[79]
First, they use the unsupervised algorithm, latent
Dirichlet allocation (LDA) model to divide keywords into themes
corresponding to specific synthesis steps. They extract informa-
tion about synthesis methods and steps of materials from more
than 2.2 million published documents, such as “grinding”,
“heating”, “dissolution” and “centrifugation”. After adding a
small number of annotations, the random forest classifier can
be associated and divided into different kinds, such as solid-state,
Figure 3. Exploring the Suzuki–Miyaura reaction using ML. a) Validation of the predictive power of the model for a test set of 30% of the reactions (1728
reactions). RMSE, root-mean-square error. b) Simulation of the ML-controlled exploration of this reaction space. The yellow bar shows the initial random
choice of 10% of reaction space (576 reactions). The green bars show the next batches of 100 reactions chosen by the ML algorithm. The error bars
represent the standard deviation within individual batches for Suzuki–Miyaura coupling. Reproduced with permission.[38]
Copyright 2018, Springer
Nature.
Figure 4. The schematic of ML process for large-scale reaction classifica-
tion. Reproduced with permission.[76]
Copyright 2014, American Chemical
Society.

hydrothermal, sol–gel synthesis, and so on. Finally, the flowchart
of the possible synthesis process is accurately reconstructed
using the Markov chain representation of the order of the exper-
imental steps. The research shows that ML method can not only
classify the synthetic process of materials accurately but also
reconstruct the synthetic route map of materials, and present
the results in a human-readable standardized way, which can
be further used to build the synthetic process database.
One of the key challenges in guiding experiments to materials
with required properties is finding ways to navigate effectively in
a wide composition and structure space. Yuan et al. applied the
active learning algorithm, one of the ML methods, to effectively
select the sample components to be synthesized and tested
in the next step of experiments by exploiting the training
data.[52]
Only through five iterations, the piezoelectric
(Ba0.84Ca0.16)(Ti0.90Zr0.07Sn0.03)O3 with the largest electrostrain
of 0.23% was synthesized. They also compared four different
experimental strategies and found that the strategy of balancing
exploration (using uncertainty) and exploitation (only using
model prediction) is more efficient in experimental design.
This idea can be widely used in the research of new materials.
There is a Chinese proverb, “Failure is the mother of
success”. Each failure brings researchers one step closer to
success. Raccuglia et al. trained ML models using data from
unsuccessful hydrothermal reactions in the laboratory, and used
the models to predict new reactions.[80]
The models were able to
successfully predict the synthetic conditions of new organic–
inorganic materials with a success rate of 89%. Literature pub-
lished by researchers in the field of chemistry usually only
include examples of successful reactions, but in fact, a large
number of unreported failed experiments also contain informa-
tion about synthetic conditions. The information contained in
Figure 5. Schematic of MCTS methodology. a) MCTS searches by iterating over four phases. In the selection phase (1), the most urgent node for analysis
is chosen on the basis of the current position values. In phase (2), this node may be expanded by processing the molecules of the position A with the
expansion procedure (b), which leads to new positions B and C, which are added to the tree. Then, the most promising new position is chosen, and a
rollout phase (3) is performed by randomly sampling transformations from the rollout policy until all molecules are solved or a certain depth is exceeded.
In the update phase (4), the position values are updated in the current branch to reflect the result of the rollout. b) Expansion procedure. First, the
molecule (A) to retroanalyze is converted to a fingerprint and fed into the policy network, which returns a probability distribution over all possible
transformations (T1 to Tn). Then, only the k most probable transformations are applied to molecule A. This yields the reactants necessary to make
A, and thus complete reactions R1 to Rk. For each reaction, the reaction prediction is performed using the in-scope filter, returning a probability score.
Improbable reactions are then filtered out, which leads to the list of admissible actions and corresponding precursor positions B and C. Reproduced with
permission.[77]
Copyright 2018, Springer Nature.

these failed experiments is also of great value in predicting the
boundary conditions of successful and failed reactions. A large
number of laboratory failure reaction data were collected.
An SVM model was trained to predict the reaction results of
the test set. The accuracy of the model was 78% and the predic-
tion of the reaction of vanadium-selenite system was achieved.
The accuracy was 79%. By transforming the SVM model into a
decision tree model for human understanding, we can further
understand the mechanism of the reaction and guide the new
synthetic reaction.
3.4. Experimental Parameter Optimization
In traditional material developments, a large number of param-
eters need to be analyzed and adjusted manually in synthesis,
processing, and device assembly processes. The efficiency is very
low and may not be able to find the optimal parameters. ML has
powerful nonlinear regression ability to find the best location in
the huge parameter space.[81]
This idea has been applied in the welding process. Friction
stir welding (FSW) is a relatively new solid-state welding pro-
cess, which has been widely used in aerospace, shipbuilding,
automobile, and other industries. Du et al. collected 108
independent experimental data from authoritative literature to
train ML models, including neural networks and decision
trees, and explored the effects of original welding parameters
such as temperature, maximum shear stress on tool pins, tor-
que and strain rate, and potential causative variables on void
formation.[82]
The results show that the two algorithms can pre-
dict the formation of defects well, and the highest prediction
accuracy is 96.6%. With this model, the optimization of param-
eters in the welding process can be completed, and the
formation of unfavorable factors such as void formation in
FSW from ML can be avoided.
Similar examples have been applied in 3D printing. Aerosol jet
printing (AJP) is a noncontact 3D printing technology, which is
often used to fabricate microelectronic devices on flexible
substrates. It has the deposition ability of special patterns, but
the complex relationship between the main process parameters
is complex, and it will have a significant impact on the printing
quality. Zhang et al. proposed a new hybrid ML method to deter-
mine the best operating process window of AJP process in
different design spaces.[83]
This method consists of classical
ML methods, including experimental sampling, data clustering,
classification, and knowledge transfer. The method is based on
the Latin hypercube sampling experiment design, and the 2D
design space is fully explored at a certain printing speed.
Then, the influence of sheath gas flow rate (SHGFR) and carrier
gas flow rate (CGFR) on the quality of printing line was analyzed
by K-means clustering method, and the optimal operation pro-
cess window was determined by support vector machine
(Figure 6). To effectively identify more operation process
windows at different printing speeds, the transfer learning
method is used to make use of the correlation between different
operation process windows. Therefore, under the new printing
speed, the number of row samples used to identify the new oper-
ation process window is greatly reduced. Finally, to balance the
complex relationship between SHGFR, CGFR and printing
speed, an incremental classification method is used to determine
a 3D operation process window. Unlike the experiment-based
quality optimization method in 3D printing technology, this
method is developed based on knowledge discovery and data
mining theory. Therefore, the knowledge of different design
spaces can be fully excavated and transmitted to optimize print-
ing line quality.
In the future, when the material synthesis process is fully
automated, it will be integrated with industrial manufacturing
4.0, such as programmable high-throughput synthesis platform
for polymers.[84]
In the early stage of this high-throughput
synthesis, ML is needed to explore the parameter space to
determine how the ratio of raw materials and the rate of catalyst
supply can be used to synthesize ideal organic compounds with
appropriate molecular weight, narrow distribution, and few
side reactions.
Figure 6. Schematic of process of printing parameters optimization via hybrid ML method. Reproduced with permission.[83]
Copyright 2019, American
Chemical Society.

3.5. Upgrading of Characterization Methods
The great advances in materials science since the last century
have been largely due to advances in representational methods,
which have enabled scientists to observe atomic-level structures
and track atomic-level movements, thus discovering more laws of
materials science. With the development of Material Genome
Project, high-throughput materials preparation and analysis with
AI will become inevitable.[85–88]
The successful application of convolutional neural networks in
deep learning has made great achievements in image recogni-
tion.[89]
This pattern-recognition ability can be easily transferred
to the image characterization of micromaterials. Electron micros-
copy and defect analysis are the cornerstones of material science
because they provide detailed insights into the microstructures
and properties of various materials and material systems. If a
powerful and flexible platform is established for automatic defect
recognition and classification in electron microscopy, the analy-
sis can be completed more quickly after image recording and
even during image acquisition. However, a large number of
images are needed to extract statistically significant information,
and recognition is still done manually, which is not only
time-consuming but also inconsistent. Recently, Li et al. obtained
information about the size and type of defects by combining ML,
computer vision, and image analysis techniques (Figure 7).[90]
At present, the performance of the program is consistent with
the manual analysis of quality. Further improvement in the pro-
gram can make real-time analysis of large datasets.
X-ray diffraction (XRD) data can also be analyzed by ML.[91]
In
the face of large-scale measurement data with high-throughput
characterization, it will undoubtedly consume a lot of time and
energy if we analyze them one by one and find sample data of
interest from them. ML can help researchers improve the
efficiency of analysis and discover hidden rules in data.
By depositing ternary Fe─Ga─Pd compound films on a
single silicon wafer, Long et al. obtained 535 samples of the
size of 1.75 1.75 mm2
with continuously changing ternary
Fe─Ga─Pd composition.[92]
The diffraction data of 273 samples
were obtained by XRD characterization. Then, with the help of
ML, 273 XRD sample data are clustered by hierarchical clustering
algorithm in unsupervised learning, and single-phase samples
are merged into the same cluster as far as possible. Only
representative sample data in each cluster are analyzed, which
greatly improves the efficiency of analysis. The aforementioned
results show that dimensionality reduction and clustering
algorithm in ML can help to efficiently analyze high-throughput
XRD data, identify the phase distribution and the intersection of
different phases, and help researchers quickly find regions of
interest.
The capacity of lithium-ion batteries decreases with the
increase inf the times of cycles. The cycle life of batteries has
always been one of the most concerned performances of battery
researchers. Severson et al. have developed a new large data-
driven model.[93]
Without analyzing the mechanism of battery
decay, the ability to use neural networks to explore the law of
high-dimensional data can predict the whole life of commercial
lithium iron phosphate/graphite batteries only by using the
charge and discharge data of the first few cycles. In the regression
setup, the author uses the first 100 cycles, and the prediction
error is only 9.1%. In the classification setup, the author uses
the data of the first five cycles, and the prediction error is only
4.9%, which achieves the accurate prediction. This brings new
opportunities for battery production, cascade utilization and opti-
mization. For example, battery manufacturers can accelerate bat-
tery development cycles, quickly validate new manufacturing
processes, and classify new batteries according to their life expec-
tancy. Similarly, consumers can estimate the life expectancy of
batteries in their electronic products. Generally speaking, the
work emphasizes the combination of data generation and
data-driven modeling, which has broad prospects in understand-
ing and developing complex systems such as lithium-ion
batteries.
Figure 7. Schematic flowchart of the proposed automated detection approach. Input micrographic images go through the pipeline of module I—Cascade
Object Detector, module II—CNN Screening, and module III—Local Image Analysis. After module I, the loop locations and bounding boxes are identified
and then further refined to remove false positives using module II. Then module III determines the loop shape and size. Reproduced with permission.[90]
Copyright 2018, Springer Nature.

ML can also help researchers get rid of the confusion in
impedance data analysis. Electrochemical impedance spectros-
copy (EIS) is a very powerful method in the research and diag-
nosis of electrochemical batteries and future electrochemical
energy storage systems. However, it is quite difficult to analyze
a large number of EIS data. Typical optimization algorithms are
not complete. In practice, it means that researchers must accu-
rately construct the equivalent circuit (EC) model, select the
appropriate initial values of the parameters of each component
of the model, and constantly verify the output in the process to
ensure the correct convergence of the fitting. Buteau and Dahn
proposed an inverse model of ML, which transformed 100 000
independent fitting optimization problems into a single optimi-
zation problem.[94]
The error rate of solving a single optimization
problem was less than 1% by applying various viewpoints in ML
literature. If an open-source system is assembled for EIS test, it
can be easily adapted to various impedance spectrograms, and
the parameters of the physical model can be reliably fitted to
the measured data. This method has high reliability, good
consistency, and no need of manual supervision. The code used
in this work can be obtained at -https://github.com/samuel-
buteau/eisfitting.
At present, material science research has been self-derided as
“stir-fried dishes”. It adds salt and water, and discovers new
materials through trial and error. By ML and high-throughput
computing, material scientists can speed up the efficiency of trial
and error and save labor.
In the future, the development of material AI may require
some free open-source software platform, which combines
the functions of AI data analysis with the appropriate operating
interface. AI could track each scientific research topic and
provide possible alternative analysis solutions for the problems
in representation. Researchers can also upload their own
experimental process and corresponding results, so as to
facilitate everyone to solve and think about the experimental
difficulties.
In conclusion, AI will not completely replace synthetic chem-
ists. Synthetic chemists will discover new reactions in practical
scientific research and expand the theoretical basis of chemistry,
but AI will certainly become a powerful assistant to chemists to
help them find synthetic routes faster and better. Supported by
existing experimental data and theoretical basis, combined with
ML technology, AI-aided material design, synthesis, characteri-
zation, and application research will greatly promote the research
efficiency of scientists in the field of materials and help the rapid
development of material science.
4. Prospects and Future
AI is making more and more contributions in materials
research.[95–100]
This article reviews the representative research
progress of materials AI including the realization details and
advantages over conventional methods. In general, the future
development of material informatics requires high-throughput
experiments, high-throughput simulation calculations, and
high-throughput characterization. The following will be the out-
look from both software and hardware aspects.
4.1. Algorithm Upgradation
ML is data analysis (statistical method) and the required data
pursues quantity, comprehensiveness, and objectivity. Previous
studies of material informatics were limited by the computed
properties without enough accuracy. The datasets composed of
more accurate experimental results will make a big difference.
However, the current experimental samples is uncomprehensive
because of the excessive centralization of hot research spots.
Fortunately, some models are suitable for dealing with small
datasets such as autoencoders, generative adversarial networks,
active learning and transfer learning.
In addition, ML models need to be translated into actual
knowledge or physical pictures to avoid the “Black Box” charac-
teristic. Calculating the average of neurons that respond to the
descriptors could provide certain interpretation. Or more explan-
atory models, such as decision trees which can reflect the impact
of relevant factors by the weights of nodes and branches of the
tree, could be applied to boost the development of materials
informatics.
4.2. Infrastructure Construction
Effective training of ML models usually requires abundant data.
Such data could come from online databases, published papers,
or high-throughput experimental equipment.
Online databases are a trend for the application of deep learn-
ing, such as ImageNet. The development of material informatics
also need similar platforms. For example, Hatakeyama-Sato et al.
built up a database to accumulate the information of electrolytes,
including ionic conductivity, transference number, and chemical
stability.[101]
Published articles also contain vast materials data.
Researchers can search for desired information easily by natural-
language-processing technology once these papers are arranged
by standardized article formats.
More sensors and software can be integrated into the high-
throughput synthesis or characterization equipment. The results
collected by these equipment are directly fed back to AI models
for the optimization of experimental parameters. Then, the
samples with ideal properties can be obtained by adjusting
the parameters. Materials informatics will finally map the
relationship between “composition-structure-property-processing-
application” through these efforts.
AI will not completely replace humans at the work of material
research but will serve as a powerful tool to accelerate the prog-
ress of materials discovery. We material researchers all need to
learn to master this tool to decrease the trial error times, solve
more difficult material problems in more fields, and find more
rules that govern the nature we live.
Acknowledgements
The authors thank their colleagues and collaborators for ongoing useful
discussions and a careful reading of the manuscript. This project was
supported by the fund from Achievements Transformation Project
of Academicians in Wuhan (2018010403011341), Wuhan Applied
Basic Research Project (2018010401011285), 4th Yellow Crane Talent
Programme (08010004), and the Fundamental Research Funds for the
Central Universities (3004131132).

Conflict of Interest
The authors declare no conflict of interest.
Keywords
artificial intelligence, chemical syntheses, machine learning, materials
science, properties predictions
Received: November 12, 2019
Revised: December 29, 2019
Published online: March 24, 2020
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