Computational Materials Science 235 (2024) 112793
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Computational Materials Science
journal homepage: www.elsevier.com/locate/commatsci
Full length article
AI methods in materials design, discovery and manufacturing: A review
Ioannis Papadimitriou ∗, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
Information Technology Institute, Centre for Technology Hellas, 6th km Charilaou Thermis, Thessaloniki, 57001, Greece
ARTICLE
INFO
ABSTRACT
Keywords:
Artificial intelligence
Materials design
Materials discovery
In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool in various
domains, including materials design and discovery. This paper provides a comprehensive review of the AI
methodologies integrated within this context, encompassing materials informatics, density functional theory,
molecular dynamics, and finite elements analysis. We further delve into the transformative role of AI within
process engineering, manufacturing, and industry 4.0, with a focus on manufacturing process optimization
techniques. Highlighting the importance of active learning, self-correcting processing, and digital twins in
the era of smart manufacturing, this review underscores the impact of big data and data quality. The paper
provides an insight into the challenges and future prospects, pointing towards the tremendous potential AI
holds for revolutionizing the field of materials science.
1. Introduction
to identify optimal material compositions and structures for specific
applications. For example, AI has been used to design materials for
energy storage, such as batteries [8] and supercapacitors [9], metallic
alloys [10], or catalysts [11] among others. In addition to predicting
and optimizing material properties, AI has also been applied in the
discovery of new materials. Through the use of ML (ML) algorithms,
researchers have been able to identify promising material candidates
from vast databases of known materials and predict their potential
properties. Overall, the state of the art in AI use in materials science and
engineering is rapidly advancing, with significant progress being made
in the ability to predict, optimize, and discover new materials. This has
the potential to greatly impact the field, leading to more efficient and
cost-effective materials design and discovery.
Industry 4.0, also known as the Fourth Industrial Revolution, refers
to the current trend of automation and data exchange in manufacturing technologies, including the use of AI and the Internet of Things
(IoT) [12]. In Industry 4.0, AI is used to automate and optimize various
tasks in Manufacturing Processes (MP), such as predicting equipment
failures, optimizing production schedules, and identifying inefficiencies
in the production line. AI is also used to analyze large amounts of
data generated by the IoT sensors in manufacturing plants, to identify
patterns and trends that can improve efficiency and reduce costs [13].
Some examples of AI applications in Industry 4.0 include predictive
maintenance (using AI to analyze data from sensors on equipment to
predict when maintenance is required [14], reducing downtime and
improving efficiency), quality control (inspection and classification of
products on the production line, improving the accuracy and speed of
During the recent years, there have been breakthroughs of AI methods used in a range of fields. One of the first fields to experience
said breakthroughs was Computer Vision technology (CV). The use
of Convolutional Neural Networks (CNNs) enabled the creation of
models that can be used for tasks such as image recognition, object
detection, semantic segmentation and image generation. Apart from CV
which has been dominating the interest in terms of AI methods use
and development, the emergence of large Natural Language Processing
(NLP) models marked an era of rapid advances in various tasks, namely
language modeling, machine translation, sentiment analysis and text
generation, during the recent years. Nowadays there is a vast range of
major industries that have seen huge advancements with the use of AI
methods, including healthcare, logistics and transportation, commerce,
food technology, banking and entertainment.
The use of Artificial Intelligence (AI) in materials science and engineering has been rapidly increasing in recent years, with advancements
in ML techniques and the growing availability of high-quality data.
This has led to significant progress in the ability to predict material
properties, optimize material design, and accelerate discovery of new
materials [1,2]. Another key area where AI has been applied is in the
prediction of material properties. ML algorithms have been used to
predict a wide range of mechanical, thermal, and electrical properties
of materials, such as the Young’s modulus [3,4], melting temperature [5,6], and conductivity [7]. Recently, AI has been also utilized
is in the optimization of material design. By incorporating constraints
such as cost and environmental impact, ML algorithms have been able
∗ Corresponding author.
E-mail address: i.papadimitriou@iti.gr (I. Papadimitriou).
https://doi.org/10.1016/j.commatsci.2024.112793
Received 27 July 2023; Received in revised form 10 December 2023; Accepted 5 January 2024
Available online 19 January 2024
0927-0256/© 2024 Elsevier B.V. All rights reserved.
Computational Materials Science 235 (2024) 112793
I. Papadimitriou et al.
Fig. 1. Conceptual overview of AI/ML methods in Materials Science.
quality control processes and process optimization (data analysis from
the production line and bottleneck/inefficiency identification, allowing
for more efficient use of resources). Before delving deeper, a conceptual
overview of AI/ML methods and techniques discussed in the paper, as
well as their applications in materials science is shown in Fig. 1.
main limitations of using ML in materials design is the availability of
high-quality data [22]. The accuracy of ML models depends heavily
on the quality and quantity of data used for training. Henceforth,
when the term ML models is used, it will denote an umbrella typically
consisting of classic algorithms such as logistic regression, decision
trees, Support Vector Machines (SVM) or K-Nearest-Neighbors (KNN).
In addition, these methods are typically black boxes, which means it
can be challenging to interpret their predictions and understand how
they arrive at their conclusions.
DL is a subset of ML that has shown great promise in materials design. DL models can learn complex patterns in data and make
predictions with high accuracy [23–26]. DL has been used to predict
material properties, discover new materials, and optimize the synthesis
of materials. One of the key benefits of using DL in materials design is
its ability to handle large and complex datasets. DL models can learn
complex correlations between material properties and their underlying
atomic structures. DL has also shown promising results in the design
of new materials with desired properties, where it can generate new
materials based on a set of desired properties. However, one of the
main limitations of using DL in materials design is its high computational cost. DL models require significant computational resources, and
training large models can be time-consuming and expensive.
In addition to ML and DL, other AI techniques have been used in
materials design. For example, evolutionary algorithms (EA) have been
used to optimize the synthesis of materials [27]. EA can be used to
search for the optimal synthesis conditions for a given material, such
as temperature and pressure. One of the key benefits of using EA in
materials design is its ability to handle complex optimization problems
with a large number of variables. However, one of the main limitations
2. Materials design and discovery
The discovery of new materials with desired properties is a key
challenge in many scientific and engineering fields. The traditional
trial-and-error approach to materials design is time-consuming and
resource-intensive, and there is a growing interest in using artificial
intelligence (AI) techniques to accelerate the discovery of new materials [15]. In this survey paper, we review recent developments in
the application of AI to materials design and discovery, including
ML, Deep Learning (DL), and other AI techniques. In recent years,
there have been several studies on the applications of AI in materials
science, including empirical interatomic potential development, MLbased potential, property predictions, and molecular discoveries using
Generative Adversarial Networks (GAN) [16].
ML has shown tremendous potential in accelerating the discovery
and design of new materials. ML models can be trained using a large
dataset of known materials and their properties to predict the properties of new materials [17–21]. One of the major benefits of using
ML in materials design is that it can significantly reduce the time and
cost involved in experimental synthesis and characterization. ML can
also help identify new materials with desired properties that have not
yet been synthesized. This can lead to the discovery of new materials
that have potential applications in various fields. However, one of the
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to selection of best practice parameters for the calculation/simulation.
Epistemic uncertainty can be reduced through further research and
experimentation, or by using more accurate models and better data.
On the other hand, aleatory uncertainty is inherent to the system being
studied and arises from the inherent randomness and variability of
the system. Even when conditions and materials are identical, each
instantiation will differ microscopically. This type of uncertainty cannot
be reduced through further research or better models and must be accounted for in the analysis of results, while a potential treatment would
be for example the description in terms of a probability distribution,
rather than the traditional average behavior.
of using EA in materials design is its inability to handle uncertainty
and noise in the data. EA can be sensitive to small changes in the input
parameters, and it can be challenging to find the global optimum for
complex problems.
Another AI technique that has been used in materials design is
Natural Language Processing (NLP) [28]. NLPs can be used to analyze
scientific literature and extract information about materials and their
properties [29]. This information can then be used to train ML models
or generate new materials with desired properties. One of the benefits
of using an NLP in materials design is its ability to handle unstructured
data such as scientific literature. NLPs can also help identify new
research directions and potential applications of materials. However,
one of the main limitations of using NLPs in materials design is the
quality of the available literature. NLP models depend heavily on the
quality and quantity of data used for training, and the accuracy of the
models can be affected by biases and errors in the data.
Utilizing AI for research purposes is gaining momentum, with platforms such as OpenAI’s ChatGPT playing a pivotal role in this transformation [30]. This shift is evident at the University of Wisconsin–
Madison, where researchers have harnessed ML to streamline the discovery of new materials [31]. Moreover, they have expanded the
application of ChatGPT to include the extraction of valuable data from
extensive scientific literature, a task traditionally known for its laborious nature. The research team, led by researchers Morgan and Polak,
devised a strategy whereby ChatGPT would scrutinize scientific papers
at a sentence level, isolating data of relevance. The gathered information was subsequently organized in a tabular format, a process that
delivered an accuracy rate of close to 90%. To achieve full automation,
the researchers embarked on ‘prompt’ engineering, a method involving
a series of questions that instructed ChatGPT to extract and subsequently validate the data. This approach prompted the AI to revisit the
information, enabling it to identify any potential inaccuracies. Morgan
and Polak’s pioneering approach marks a departure from traditional
programming methods. It emphasizes the use of natural language as
the primary tool for AI interaction, thereby making the technology
more accessible and reducing the need for deep technical knowledge.
Importantly, the adoption of AI in research does not render researchers
obsolete but rather equips them with the means to undertake previously
unachievable projects due to limitations in time, funding, or personnel.
Thus, the integration of AI, as exemplified by Morgan and Polak,
not only boosts efficiency but also paves the way for new scientific
exploration opportunities.
Further research is needed to fully realize the potential of AI in
materials design, and to address the challenges and limitations discussed in the literature. Some potential areas for future research include
the development of new AI algorithms and methods for materials
design, the integration of AI with physical models and simulations, the
application of AI to a wider range of materials and applications, and
the development of strategies for addressing ethical and societal issues
in the use of AI in materials design.
AI has become an important tool for materials design, and it offers
significant potential for advancing materials science and engineering.
However, there are also challenges and limitations to the use of AI in
materials design, including the need for high-quality training data, the
difficulty of interpreting and explaining the results of AI algorithms,
and the potential for bias. These challenges will need to be addressed
in order to fully realize the potential of AI in materials design. In this
comprehensive review, we will present the state-of-the-art applications
of artificial intelligence in the field of materials discovery, exploring
the methodologies incorporated in these practices, and addressing the
observed challenges.
Finally, uncertainty is a key concept in modeling. There exist two
types of uncertainty: epistemic and aleatory. Epistemic uncertainty
arises from a lack of knowledge or understanding of the system being
studied, such as incomplete or inaccurate data or an incomplete understanding of the underlying physical phenomena, pertaining mainly
2.1. Materials informatics
Materials informatics is an emerging multidisciplinary field that
combines materials science with information science to facilitate the
discovery, development, and manufacture of new materials [32]. This
convergence of disciplines is enabled by the significant advancements
in computational power, ML, and data storage capabilities seen in
recent years. The field aims to extract valuable insights from large
and complex datasets generated in materials science, effectively transforming the conventional trial-and-error approach into a systematic,
data-driven one [32]. Historically, the development of new materials has been a labor-intensive and time-consuming process. It often
involves numerous experimental iterations to understand the relationships between the material’s structure, processing methods, and properties. Materials informatics has the potential to revolutionize this
process by leveraging computational tools to model these relationships,
thereby reducing the time and cost associated with materials development [32]. Furthermore, materials informatics plays a critical role
in the context of Industry 4.0, which envisions the digital transformation of manufacturing. The ability to predict material properties and
behaviors can significantly enhance the efficiency and quality of MPs,
leading to improved product performance and reduced environmental
impact [32].
The foundation of materials informatics lies in the collection, analysis, and interpretation of massive datasets related to materials [33]. It
fundamentally involves three interconnected components: data generation, feature extraction, and handling the complexity and heterogeneity
of materials data. Data Generation in Materials Science refers to the
collection and preparation of data for analysis. Data can come from various sources such as experimental studies, computational simulations,
and even scientific literature. With advancements in experimental and
computational techniques, materials science is now able to produce vast
amounts of data spanning different scales, from atomic to macroscopic,
and different properties, from mechanical to optical [34].
Feature extraction and selection is a critical step in materials informatics that involves the identification and selection of features (also
known as descriptors) that characterize the materials of interest. Features could include physical, chemical, and structural attributes. The
chosen features should capture the essential properties of the materials
and their relationships with the properties of interest. ML algorithms
are typically used for this purpose [15]. Materials data is often highdimensional, meaning it involves a large number of features for each
sample. This high dimensionality can present challenges in data analysis and interpretation, including issues related to overfitting and the
curse of dimensionality. Techniques such as dimensionality reduction
can help mitigate these problems [35]. Dealing with heterogeneity
and complexity of materials data: Materials data is typically heterogeneous, with different types of data (e.g., text, images, numerical
data) needing to be integrated for analysis. Moreover, the data often
captures complex, non-linear relationships among variables. Addressing
this complexity and heterogeneity requires advanced data integration,
data mining, and ML techniques [32].
In materials informatics, a multitude of techniques are leveraged
to analyze and interpret data. These include various ML strategies,
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transfer learning, natural language processing, and data mining. ML
provides a powerful set of tools for predicting material properties and
discovering underlying patterns in data. These tools are supervised,
unsupervised and semi-supervised learning, transfer learning and NLP.
Materials informatics offers powerful tools and methods that can
be applied to various aspects of materials science and engineering.
By using ML models to predict material properties based on various
features, materials informatics can significantly speed up the process
of materials discovery and design. This approach allows for the exploration of a larger design space in a shorter amount of time, leading
to the discovery of materials with novel or superior properties [17].
Furthermore, materials informatics can be used to develop predictive
models that can forecast the properties of materials based on their
composition and structure. This capability can be leveraged to optimize
material selection for specific applications, potentially improving the
performance and efficiency of the resulting products [32]. Moreover,
by combining domain knowledge with ML techniques, materials informatics can help design experiments that are more likely to yield
useful results. This approach can lead to more efficient use of resources
and faster achievement of research objectives [36]. Finally, in the
manufacturing setting, materials informatics can be used to monitor
the production process and detect any anomalies or deviations from
the expected quality standards. This application can help prevent the
production of defective products, reduce waste, and improve overall
manufacturing efficiency.
Materials informatics represents a transformative approach in the
field of materials science and engineering. By leveraging advanced computational techniques, including ML and data mining, this emerging
discipline provides an efficient pathway for accelerating materials discovery and development. Central to materials informatics are principles
such as data generation, feature extraction, and the management of
high-dimensionality and complexity in materials data. Utilized in areas
such as predictive modeling of materials properties, optimal experiment
design, and quality control in manufacturing, materials informatics
showcases its potential to revolutionalise the materials science field.
(MD) algorithms serve as a more practical approach in larger scales.
Finally, when at the macroscopic scale, continuum methods that essentially approximate the conservation relations between mass, energy
an momentum, offer a more expeditious but complimentary view [38].
Sophisticated calculations such as DFT are computationally intensive
and require massive amounts of computing power (manifesting in the
use of high performance computers, requiring large scale collaborations
at times). This is a very apt example of the expeditious effect the AI
can have in materials discovery. AI has the capacity to rationalize
large amounts of data and detect the underlying correlations between
them with relatively few data points (compared to the traditional approaches), thus make valuable predictions and help navigate uncharted
composition space landscapes. With the increasing interest in developing AI tools for DFT modeling, there has been a growing emphasis on
the importance, primarily, of data quality.
Only recently Merchant et al. a research team from Google’s Deepmind unveiled a novel deep learning tool, Graph Networks for Materials
Exploration (GNoME), designed to accelerate and expedite the identification of stable crystals for potential applications in various technologies [39]. The researchers use two frameworks, involving structural
modifications and compositional predictions, generating over 109 candidates, which are then filtered, ranked, and evaluated using GNoME,
ultimately assessed through DFT computations for stability and precision 2. GNoME’s predictions encompassed 2.2 million new crystals,
with 381,000 newly recognized (almost an order of magnitude larger
than previous work) as highly stable and suitable for experimental synthesis for a total of 421,000 stable crystals. This breakthrough, equivalent to 800 years’ worth of conventional knowledge, involved materials
with potential uses in superconductors, supercomputers, advanced batteries, and more. GNoME utilized AI to forecast material stability
with an unprecedented scale and accuracy. External researchers have
already experimentally realized 736 of the predicted structures, highlighting the tool’s efficacy. The discovered materials exhibit promise
for the development of sustainable technologies, including enhanced
electric vehicle batteries and more efficient computing.
Data quantity is also crucial for properly coupling AI with DFT.
Property mapping and high throughput screening by combining DFT
with AI is an approach that has gained significant attention in recent
years. This involves predicting the properties of materials based on
their electronic structures using ML models. The ML models are trained
on a large dataset of DFT calculations to learn the relationship between
the electronic structure and the material properties of interest. Once
trained, these models can be used to rapidly predict the properties of
new materials without the need for computationally expensive DFT
calculations. A study by Meredig et al. [40] used an ML-based approach
to screen over 1.6 million compounds and reveal 4500 previously
unknown ternary compounds, in which the AI algorithm required no
knowledge of crystal structure and operated at six orders of magnitude
lower computational expense. This technique would lay the foundation
for entirely new phase diagrams (or suggest additional contributions
to existing ones), accelerate the process of interatomic potentials construction (as will be discussed below), and provide a large list of
interesting new compositions that may now be mined for technological
applications. Overall, property mapping and screening in DFT with AI
have the potential to greatly accelerate the discovery and design of
new materials with desired properties, especially for applications where
high-throughput screening of large databases is required.
Apart from expeditious interpolation and high-throughput screening, AI can help first principles methods in a more fundamental level.
Density Functional Theory (DFT) has been a cornerstone for ab initio
computational studies, predicting a wide range of material properties
by calculating the electronic structure of a material. There are many
different types of DFT functionals, each with different levels of accuracy
and computational cost. The choice of functional depends on the specific system being studied and the desired level of accuracy. Functionals
are used to calculate the energy and other properties of a system based
2.2. Density functional theory
Density functional theory (DFT) is a widely used method in computational chemistry and physics for predicting the electronic structure
and properties of materials and molecules. It involves solving the
Schrödinger equation for a system of interacting electrons, using approximations to account for the many-body nature of the electron
interactions. In recent years, there have been efforts to use artificial
intelligence (AI) techniques to improve the efficiency and accuracy of
DFT calculations. Other approaches involve using AI to optimize the
choice of functional (approximation) used in DFT calculations, or to
learn the exchange–correlation functional directly from data. Overall,
the use of AI in DFT has the potential to significantly reduce the computational cost of predicting the electronic structure and properties of
materials and molecules, and could enable the study of larger and more
complex systems than is currently possible. However, these methods are
still an active area of research and development, and there is ongoing
work to improve their accuracy and applicability.
As DFT is essentially a first principles calculation approach, it is
very sensitive to the parameters used in order to achieve the optimal balance between accuracy and computation intensiveness. When
enough data are available the latter can be alleviated by interpolating
between known structures and their properties using AI techniques.
A well documented example is the case of diffusion modeling. It is a
fundamental phenomenon, present in all states of matter and consists
of the transport of atoms and molecules from high to low concentration
regions [37]. As is the case with materials discovery in most fields, for
many years, the discovery of novel materials was an arduous process of
trial and error where luck was a decisive factor [38]. At the electronic
level, DFT calculations play a central role, while Molecular Dynamics
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Fig. 2. A summary of the GNoME-based discovery.
Source: The image is reproduced under the CC-BY
4.0 License from Ref. [39].
computational cost of DFT calculations, pseudopotentials are used to
replace the core electrons with an effective potential that accurately
models their behavior. This allows for more efficient calculations while
still capturing the important electronic behavior of the material. There
are different types of pseudopotentials, including ultrasoft [45] and
projector-augmented wave (PAW) pseudopotentials [46]. The use of AI
in DFT pseudopotential development stems from ML algorithms, which
can predict materials properties by training on large datasets. The use
of AI techniques, such as neural networks, has shown promising results
in predicting pseudopotential parameters and enhancing transferability. Woo et al. introduced a deep neural network-based method for
generating highly transferable local pseudopotentials (LPPs) for rapid
quantum simulations, overcoming limitations of traditional LPPs by
explicitly incorporating norm-conserving conditions in a loss function
and demonstrating superior performance and transferability across sand p-block elements [47].
To summarize, as AI continues to advance, it is expected to play
a more prominent role in materials modeling and the development of
more accurate and efficient DFT methods. Fast interpolation and highthroughput screening, as well as faster pseudopotential and functional
development can help bypass the constraints of ab initio techniques
and its trade-off between time consumption and materials modeling
accuracy, leading to the faster discovery of novel materials tailored to
specific sets of properties.
Fig. 3. The xc energy density is constructed using the fully-connected NN, which takes
the spatially local descriptors.
Source: The image is reproduced under the CC-BY 4.0 License from Ref. [43].
on its electron density. In other words, they determine the total energy
of a system as a functional of its electron density. This is done by
solving the Kohn–Sham equations [41], which are a set of equations
that describe the behavior of the electrons in the system. The first proof
of principle for AI use in DFT appeared ten years ago, when a simple
kernel ridge regression method was used to find an approximation of
the functional by training on results from accurate numerical calculations for a simple problem, such as the kinetic energy of non interacting
fermions in a one-dimensional box [42]. Nagai et al. demonstrated that
deep learning techniques, applied to a limited dataset of molecules,
could facilitate the creation of a Meta-Generalized Gradient Approximation (Meta-GGA) exchange–correlation functional. Remarkably, this
newly designed functional could be extended to accurately describe
hundreds of molecules containing first- and second-row elements, maintaining the precision of conventional functionals. This accomplishment was made possible by employing a versatile feed-forward neural
network to link density and energy (see Fig. 3) [43].
Kirkpatrick et al. [44] have introduced a novel approach to density
functional development by addressing the fractional electron problem.
By incorporating AI into their methodology, they have made significant
strides in creating more accurate density functionals. The authors used
a combination of ML algorithms and quantum Monte Carlo calculations
to develop new functionals capable of accurately describing fractional
electron systems. Their approach has demonstrated potential for the
design of advanced materials and development of more accurate DFT
methods. AI has had a significant impact on the development of DFT
pseudopotentials, leading to improvements in accuracy and efficiency.
The work of Kirkpatrick et al. [44] on solving the fractional electron
problem has pushed the frontiers of density functional development by
leveraging AI techniques.
Pseudopotentials, which simplify the calculation of the electronic
structure, play a crucial role in DFT simulations. They are used to model
the behavior of the core electrons in a material. Core electrons are the
electrons in an atom’s innermost shells, and they are responsible for
the atom’s chemical properties. However, they are much more tightly
bound to the nucleus than the valence electrons, which are the electrons
in the outermost shell. As a result, they require a lot of computational resources to accurately calculate their behavior. To reduce the
2.3. Molecular dynamics
Molecular dynamics (MD) is a powerful computational technique
for simulating the behavior of molecules and materials over time.
However, accurate modeling of the interactions between atoms and
molecules is crucial for obtaining reliable results from MD simulations.
Traditional force field development requires a combination of experimental data and empirical rules, which can be time-consuming and
computationally expensive. AI and ML techniques have shown great
potential in the development of accurate and efficient force fields for
MD simulations.
One approach to AI-assisted MD simulations is the use of computer
vision methods. These methods can be used to extract information from
large datasets of molecular structures, such as images or videos of
molecules. For example, the CNN can be used to identify and classify
different types of molecules based on their structural features. The CNN
has been successfully applied to the classification of small molecules,
as well as the prediction of protein-ligand binding affinities [48,49].
Other computer vision methods, such as autoencoders and generative
adversarial networks (GANs), can be used for the generation of new
molecular structures with desirable properties [50,51].
In addition to computer vision methods, AI and ML algorithms can
also be used for the development of more accurate and efficient force
fields. For example, neural networks can be used to learn the underlying relationships between atomic positions and forces, allowing for the
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Fig. 4. Concept of the surrogate FEA approach. On the left, the numerical predictions before applying the ML approach. After the ML algorithm is applied, the numerical prediction
is a better representation of the actual system.
Source: The image is reproduced under the CC-BY 4.0 License from Ref. [59].
development of more accurate force fields [52]. These ML models have
been employed for optimizing potentials, leading to more accurate and
efficient simulations [53]. Support vector machines (SVMs) can be used
to predict the thermodynamic properties of materials based on their
atomic structures [54]. An example of the successful application of AI
in MD simulations is the work of Gastegger et al. [55], who developed
a neural network potential (NNP) for the simulation of molecular
dynamics. The NNP was trained on a dataset of over 20,000 organic
molecules and was shown to accurately predict molecular structures,
energies, and forces. The use of the NNP significantly reduced the
computational cost of the MD simulations, while maintaining a high
level of accuracy. Moreover, deep learning algorithms, such as graph
neural networks, can be used to model complex chemical reactions and
dynamics [56,57].
AI-assisted MD simulations have also been used for the discovery of
new materials and molecules. The use of AI and ML algorithms can accelerate the screening of large databases of molecules and materials for
their properties and potential applications. For example, the Materials
Project database uses AI and ML techniques to predict the properties
of thousands of materials, including their mechanical, thermal, and
electronic properties [58]. The database has been used for the discovery
of novel materials with desirable properties.
In conclusion, AI and ML techniques have shown great promise
in the field of MD simulations, particularly in the development of
accurate and efficient force fields and the discovery of new materials
and molecules. The combination of computer vision methods, neural
networks, SVMs, and deep learning algorithms can provide a powerful
tool for advancing the field of materials science. However, further
research is needed to address the challenges of large and diverse
datasets, appropriate training algorithms, and the interpretation of
results obtained from AI-assisted MD simulations.
Another application of AI in FEA is in the sphere of mesh refinement
optimization [62]. The incorporation of AI algorithms has empowered
FEA software to intuitively adapt the mesh resolution to the complexity
of the problem. This adaptation not only enhances the accuracy of the
analysis but also significantly reduces the computational cost, leading
to a more efficient process. This adaptive capability of AI in FEA has
proven to be particularly valuable in complex engineering problems
where the mesh resolution plays a critical role in determining the
accuracy of the analysis.
Furthermore, AI has also been used in failure prediction and design
optimization in FEA [63]. By using predictive models and optimization
algorithms, AI can help engineers anticipate potential failures in the
design phase and optimize the design to improve its performance and
durability. This can significantly reduce the cost and time required for
prototyping and testing, making the design process more efficient and
effective.
Overall, the state of the art in AI usage in FEA is rapidly progressing,
with substantial advancements being made in areas like material property prediction, mesh refinement optimization, failure prediction, and
design optimization. This amalgamation of AI and FEA holds promising
potential and is poised to bring about a significant transformation in
the field of engineering analysis, leading to more accurate, efficient,
and cost-effective FEA.
2.5. PINNs
The integration of physical laws into ML models, a concept known
as PINNs, was first introduced in 2018 by Raissi et al. [64]. This idea
stemmed from the realization that incorporating known physics into the
architecture and loss function of neural networks could solve complex
differential equations. The initial concept of combining physics-based
knowledge with ML models, however, originated in the 1980s [65].
PINNs are a type of Deep Neural Networks (DNNs) that are trained
to solve supervised learning tasks while adhering to any given law of
physics represented by general nonlinear Partial Differential Equations
(PDEs) [64]. The training of these networks is performed by constructing a loss function composed of a data-fidelity term, which measures
the model’s data fit, and a physics-informed term, which penalizes
predictions not adhering to the physical laws [64].
The primary function of PINNs is to approximate solutions to PDEs.
The difference between the predicted and actual outputs is minimized
by adjusting the weights and biases of the neural network through
backpropagation. The distinctive feature of PINNs is the integration
of known physical laws as auxiliary constraints in the network’s loss
function. This means that the training process not only minimizes
the discrepancy between model predictions and observed data but
also ensures that the model predictions conform to known physical
laws [64].
Traditionally, numerical methods have been used to solve complex
differential equations in materials science, which involves studying the
properties and behavior of materials. These equations can describe a
variety of phenomena, from the behavior of materials under stress to
the diffusion of atoms or the flow of heat. However, these traditional
methods can be computationally expensive and may not provide a
global solution. PINNs can revolutionize materials science by offering
2.4. Finite elements analysis
AI has been making significant strides in diverse sectors, including
Finite Element Analysis (FEA). As we advance technologically, the integration of ML into FEA has noticeably amplified in recent years [59].
This symbiosis of AI and FEA is largely propelled by the development
of sophisticated ML techniques and the availability of high-quality, diverse datasets. This integration has catalyzed significant advancements
in improving both the precision and efficiency of FEA by seamlessly
incorporating ML algorithms into the analytical process [60].
A pivotal domain where AI’s role in FEA is quite conspicuous is in
the prediction of material properties [61]. ML algorithms have been
ingeniously employed to predict a wide range of properties including
mechanical, thermal, and electrical characteristics of materials [6]. This
includes attributes such as Young’s modulus [3,4], melting temperature [5,6], and conductivity [7] among others. Surrogate FEA models
have been used to successfully provide a mid-fidelity estimate of the
material’s response at any time or location of interest, by employing
supervised ML algorithms (Fig. 4) [59]. These algorithms have proven
their worth by displaying a high degree of accuracy in their predictions.
High precision has led to more accurate and efficient FEA, largely diminishing the need for exhaustive experimental testing, thereby saving
valuable resources and time.
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Lastly, CV is a very useful area of research to manufacturing as it can
enable smarter, faster and more efficient quality control [71]. Also, it is
an integral part of the Collaborative Robots (Cobots) design [72]. These
are specifically designed for direct interaction with a human within
a defined collaborative workspace; CV plays an important role in the
process of creating collaborative environments between humans and
robots.
efficient and accurate solutions to these problems. By solving the
governing PDEs and respecting the underlying physics, PINNs can
predict the behavior of materials under various conditions. This enables
rapid screening of material properties and conditions, accelerating
materials design and discovery. Moreover, PINNs can be instrumental
in discovering new materials by predicting their properties based on
governing physical laws. This capacity to design materials with desired
properties can significantly impact various industries, from electronics
to aerospace [66].
In addition to their ability to solve complex differential equations,
PINNs can also provide interpretability, a significant advantage over
conventional neural networks. Because PINNs incorporate physical
laws into their architecture, it is possible to trace how these laws
influence the network’s predictions. This transparency can yield insights into the relationship between input variables and the predicted
output, enhancing our understanding of the physical phenomena being
modeled [67]. Furthermore, by incorporating physical laws, PINNs
can better generalize and make reliable predictions even in regions
of the input space where data might be scarce or unavailable. This
interpretability and generalizability make PINNs particularly powerful
tools in scientific research and industry [67]. Overall, PINNs represent
a groundbreaking tool in the intersection of computational physics and
ML. By integrating physical laws into neural networks, they offer robust
and reliable predictions, significantly advancing materials design and
discovery.
3.1. Manufacturing process optimization techniques
AI is a rapidly growing field with numerous applications in various
industries. One such industry is manufacturing, where AI has the potential to revolutionize the entire process [73]. In simple terms, AI can be
defined as the ability of machines to perform tasks that would normally
require human intelligence, such as recognizing patterns or making
decisions. The role of AI in MP is to optimize the production cycle
by automating tasks, predicting outcomes, and improving efficiency.
This technology can be used in a wide range of applications, from
optimizing the supply chain to predicting equipment failures. With
AI, manufacturers can reduce waste, increase production speed, and
improve quality, all while lowering costs [74].
The importance of AI in MP is increasing day by day. This is because
of the numerous benefits it offers, such as increased productivity, lower
costs, and improved quality. By using AI, manufacturers can make
more informed decisions, reduce errors, and increase the accuracy of
predictions [74]. This has the potential to transform MP and make them
more efficient and effective. These processes have traditionally been
based on well-established techniques that have been refined over the
years to improve efficiency and reduce errors, while they are typically
designed to produce high-quality products at a reasonable cost [74].
Before the advent of AI, conventional optimization techniques were
used to improve these processes. Some of the most widely used techniques include statistical process control (SPC) and Design of Experiments (DoE) [75]. SPC involves monitoring and controlling the quality
of a product or process by using statistical methods. DoE, on the other
hand, is a structured method of identifying and testing the critical
factors that affect the quality of a product or process. While these conventional optimization techniques have been successful in improving
MP, they also have their limitations. SPC can be time-consuming and
requires a large amount of data to be effective. DoE, on the other hand,
can be expensive and may not be suitable for all types of MP.
Evolutionary algorithms, such as genetic algorithms and particle
swarm optimization, have been employed in MP optimization [76].
These algorithms are inspired by natural selection and mimic the
evolutionary process to optimize the MP. Genetic algorithms are used
for optimization problems with discrete variables, while particle swarm
optimization is applied to continuous variable optimization problems.
While these AI techniques have shown great potential in MP optimization, they also have limitations. The performance of these techniques
is highly dependent on the quality and quantity of data, and their
interpretability can be a challenge.
Another limitation of conventional optimization techniques is that
they do not always account for the complex interactions between
different factors that affect the quality of a product or process. By
using AI algorithms and predictive analytics, AI can identify patterns
and correlations in large datasets that conventional techniques may
miss. ML techniques have gained significant attention in MP optimization [77]. Supervised learning, unsupervised learning, and RL are the
three major types of ML techniques used in MP. Supervised learning is
utilized for classification and prediction, while unsupervised learning
is used for clustering and dimensionality reduction. Furthermore, RL
is applied in MP for decision-making and control. Deep learning is a
type of ML that uses neural networks with multiple layers to model
complex relationships in data. CNNs and RNNs are two popular types
of deep learning techniques used in MP. CNNs are used for image and
3. Process engineering, manufacturing and Industry 4.0
Manufacturing also stands to gain from next level advancements
in AI and the improved efficiency they bring in predictive maintenance, production robotization and optimization and quality control.
The recent Industry 4.0 paradigm shift has integrated AI as a leading
component of the industrial and manufacturing transformation. AIassisted industrial production systems can outperform humans, while
in some cases robots can complete tasks that a human could not or
would not perform, such as manipulation of hazardous materials.
ML is a subset of AI that enables learning from data via pattern
recognition. ML methods are typically categorized in unsupervised,
supervised, semi-supervised and RL approaches. Approaches such as
Support Vector Machines (SVM), Principal Component Analysis (PCA),
Gaussian Mixture Models (GMMs), K-Nearest Neighbors (KNN) and
Artificial Neural Networks (ANNs), as well as the fusion of the aforementioned methods [18] have been used for descriptive, predictive and
prescriptive maintenance and analytics in Industry 4.0 [19]. Methods
such as Decision Trees (DTs), Gradient Boosting (GB), clustering, Kmeans, Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) and ANNs have been utilized for early failure detection [20]
and intrusion detection [21].
Deep Learning (DL) is a subfield of ML that consists of multi-layered
models that are typically trained on large datasets, which allows the
model to learn patterns and relationships within the data. These networks are typically composed of multiple layers of interconnected
nodes, which process and transform the input data as it flows through
the network. Convolutional (CNNs) and Recurrent Neural Networks
(RNNs), autoencoders and Deep Belief Networks are amongst the algorithms that have been used in Industry 4.0 (maintenance, analytics,
early fault detection and fault diagnosis) [23–26]
Language models enable a system or a machine to understand the
human language and extract insights from it. The advent of attentionbased methods [68] and transformers [69] has marked the era of
data-driven agents with systems such as GPT-3 [70]. NLP systems have
recently started finding their way into the manufacturing industry as
well. Attention-based models can be used for the interpretation of
natural language to structure machine language (e.g. SQL [28]) and
vice versa.
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algorithms can adjust the printing parameters in real-time, ensuring the
optimal quality of the part. In 3D printing, AI is also being used to
develop new materials and alloys, enabling the creation of stronger,
more durable, and more versatile parts [83]. By analyzing the properties of different materials and their performance in different printing
processes, AI algorithms can develop new alloys and composites that
are optimized for specific applications, such as aerospace or medical
implants.
In nanomanufacturing, AI has revolutionized the field by enabling
the precise control and manipulation of materials at the nanoscale [84].
Nanomanufacturing involves the production of materials and devices
with dimensions on the scale of nanometers, where the properties of the
material can differ significantly from those at the macroscopic scale.
AI algorithms can control the properties of materials and manipulate
them to form complex structures and devices with unprecedented
precision. One of the applications of AI in nanomanufacturing is in the
development of new sensors and devices that can detect and respond
to stimuli at the nanoscale. These devices have the potential to revolutionize fields such as biomedicine and environmental monitoring,
enabling the detection and treatment of diseases and pollutants with
high precision. The integration of AI in additive manufacturing and
nanomanufacturing has enabled the development of new materials,
processes, and devices that were previously unattainable. AI algorithms
can optimize the design of parts and structures, monitor and control
the printing process, and develop new materials and alloys. These advancements have the potential to revolutionize fields such as aerospace,
biomedicine, and environmental monitoring.
Looking to the future, the implementation of AI in MP is expected
to increase significantly, bringing with it new opportunities and challenges. Among these challenges are the need for skilled workers, the
ethical implications of using AI in decision-making, and ensuring the
security of manufacturing systems. Overall, the use of AI in MP has
the potential to revolutionize the industry by improving efficiency,
reducing costs, and increasing productivity. By embracing these new
technologies and leveraging them effectively, manufacturers can gain
a competitive advantage in the global marketplace.
signal processing, while RNNs are utilized for time-series analysis and
sequence data.
AI can be used for decision support in MP, particularly in areas
such as production planning, scheduling, and inventory management.
AI techniques such as RL, decision trees, and Bayesian networks can
be used to analyze data, identify patterns, and make predictions [77].
These techniques can help manufacturers make informed decisions
about production, reduce waste, and optimize inventory levels.
One of the main benefits of AI in MP is the ability to automate tasks
that are repetitive, time-consuming, and prone to error. AI-powered
robots and machines can perform tasks such as assembly, material
handling, and quality control. This can help reduce costs, improve efficiency, and enhance safety in the MP. AI techniques such as linear programming, optimization algorithms, and predictive maintenance can
be used to identify areas of improvement and implement cost-saving
measures [77].
Quality control is an essential aspect of MP. AI can be used to
improve the accuracy and efficiency of quality control processes, such
as defect detection and classification, using techniques such as image recognition and machine vision [78]. AI-powered quality control
can help manufacturers improve product quality, reduce defects, and
increase customer satisfaction.
The impact of AI has been significant in various traditional manufacturing sectors, such as the automotive industry, aerospace industry,
and consumer goods industry. The automotive industry has been one
of the primary beneficiaries of AI in recent years. AI has helped
manufacturers to optimize their MP and improve the quality of their
products. For instance, the use of AI in predictive maintenance has
helped manufacturers to reduce downtime and improve production
efficiency [77]. AI-powered robots have also been used in various
automotive MP such as welding and painting, resulting in improved
accuracy and speed [79]. Furthermore, AI has been used in the development of self-driving cars, which have the potential to revolutionize
the transportation industry. Furthermore, the aerospace industry has
also benefited significantly from the use of AI. AI has helped aerospace
manufacturers to optimize their production processes and improve the
safety and performance of their products [80]. For example, AI has been
used in the design and development of aircraft, resulting in improved
aerodynamics and fuel efficiency. AI-powered robots have also been
used in various aerospace MP such as drilling and riveting, improving
accuracy and reducing lead times [81]. Additionally, AI has been
used in predictive maintenance of aircraft engines, helping to reduce
maintenance costs and improve safety. Finally, the consumer goods
industry has also been impacted by the use of AI in MP. AI has helped
manufacturers to optimize their production processes and improve
product quality. For instance, AI has been used in the development of
new products, resulting in improved design and functionality [82]. AI
has also been used in supply chain management, helping manufacturers
to improve their inventory management and reduce costs.
Additive manufacturing, also known as 3D printing, is an advanced
MP that has seen a surge in popularity over the past decade due to its
ability to produce complex geometries in a cost-effective manner. The
integration of AI in additive manufacturing has enabled the production
of parts with greater precision, accuracy, and consistency. AI has also
facilitated the development of new materials and processes that were
previously unfeasible or impractical to manufacture [83]. One of the
significant benefits of AI in additive manufacturing is the ability to
optimize the design of parts and structures. AI algorithms can analyze
the performance of different designs, and use this information to generate optimized designs that are more efficient, lighter, and cheaper
to manufacture. This optimization can lead to the development of new
products that were previously unattainable, such as complex lattices
and intricate geometries. Moreover, AI can be used to monitor and
control the printing process, ensuring that the part is printed correctly
and according to the desired specifications. By monitoring various
parameters, such as temperature, humidity, and material flow rate, AI
3.2. Active learning
Active learning is a ML method that involves actively selecting the
most informative examples to label, with the goal of improving the
performance of a ML model. In the context of materials design, active
learning can be used to improve the performance of ML algorithms that
are used to explore the space of possible materials and optimize their
properties [85,86].
One of the key challenges in materials design is the vastness of the
materials space, which includes an enormous number of possible combinations of chemical elements, structures, and properties. Traditional
methods for exploring this space, such as experiments and simulations,
are time-consuming and expensive, and they are limited by the human
ability to generate and test hypotheses. ML algorithms can help to
overcome these limitations by enabling the rapid and efficient exploration of the materials space, but they require large amounts of labeled
data in order to learn the relationships between materials properties
and their performance in specific applications. Active learning can be
used to address this challenge by enabling ML algorithms to select
the most informative examples to label, based on their potential to
improve the performance of the model [87]. This can be achieved by
using an active learning algorithm to select the examples that are most
uncertain or most informative, according to a selected criterion, and
then labeling these examples and adding them to the training dataset.
By iteratively selecting and labeling the most informative examples,
active learning can help to improve the performance of a ML model
for materials design, and it can also reduce the amount of labeled data
that is required to achieve a given level of performance.
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Overall, active learning is a powerful and effective method for
improving the performance of ML algorithms for materials design.
By enabling ML algorithms to actively select the most informative
examples to label, active learning can help to reduce the amount of
labeled data that is required, and it can also improve the accuracy,
interpretability, and generalizability of the learned models [86].
The use of active learning in industrial processes has been growing in recent years, with advancements in ML techniques and the
growing availability of data. Active learning refers to the ability of a
ML algorithm to actively select the most informative data points for
training, rather than relying on a fixed dataset. This has the potential to
greatly improve the efficiency and effectiveness of industrial processes,
by reducing the amount of data required for training and enabling
real-time adaptation to changing conditions.
One key area where active learning has been applied is in predictive
maintenance for industrial equipment. By actively selecting data points
for training, ML algorithms have been able to accurately predict when
equipment is likely to fail, allowing for proactive maintenance and
reducing downtime. This has been particularly useful in industries
such as manufacturing, where equipment failures can have significant
financial and operational impacts.
Another area where active learning has been utilized is in process
control and optimization. By actively selecting data points for training,
ML algorithms have been able to adapt to changing process conditions
and optimize control parameters in real-time. This has been particularly
useful in industries such as chemical manufacturing and oil refining,
where process conditions can vary widely and require precise control.
Overall, the state of the art in active learning for industrial processes
is rapidly advancing, with significant progress being made in the ability
to adapt to changing conditions and optimize control parameters. This
has the potential to greatly impact industrial processes, leading to
improved efficiency and effectiveness.
has been employed for real-time optimization of injection molding
processes, leading to reduced waste and improved product quality [99].
AI has been applied in self-correcting processing across various industries, with a particular focus on materials manufacturing. AI can be
used to optimize production processes, identify defects, and maintain
consistent product quality [100]. For example, AI-based self-correcting
systems can optimize injection molding processes, reducing waste and
improving product quality [101]. AI-driven techniques can assist in defect detection [102], process parameter optimization [103] and online
predictive maintenance [14].
Despite these benefits, challenges associated with AI in selfcorrecting processing include the need for high-quality data, the complexity of AI models, and concerns about trust and transparency [104].
The future of AI in self-correcting processing holds great promise
for improving efficiency, reliability, and safety across industries. As
AI algorithms and computational power advance, it is expected that
AI-based self-correcting systems will become more capable and versatile, enabling them to tackle a wider range of complex tasks and
systems. In materials manufacturing, this could lead to significant
advancements in production efficiency, product quality, and sustainability. Despite the potential benefits of AI in self-correcting processing,
several challenges need to be addressed. High-quality and diverse
data is crucial for training accurate and robust AI models. The lack
of such data can lead to biased or ineffective AI systems [105]. AI
models, particularly deep learning algorithms, can be complex and
difficult to interpret. Ensuring that AI-driven self-correcting systems
are transparent and understandable is essential for building trust and
facilitating human oversight [106]. Integrating AI-based self-correcting
systems with existing infrastructure and processes can be challenging,
requiring modifications to existing workflows and the development of
new interfaces and protocols [107].
3.3. Digital twins
3.2.1. Self-correcting processing and smart manufacturing
Self-correcting processing is a technology that enables systems to
monitor, analyze, and adjust their operations in real-time to maintain
desired outcomes [88]. These systems identify deviations from expected
performance and automatically make corrections to ensure optimal
functioning [89]. Self-correcting processing is particularly relevant to
materials manufacturing, where maintaining efficient and reliable operations is crucial for productivity, safety, and sustainability [90]. This
technology is also applicable to various other industries, including
transportation, healthcare, and energy.
Before the advent of AI, self-correcting processing techniques relied on traditional control algorithms, such as proportional–integral–
derivative (PID) controllers, adaptive control, and model predictive
control [91]. These techniques utilized mathematical models to predict
system behavior and make adjustments based on predefined rules and
feedback loops [92]. However, these approaches often required extensive domain knowledge and manual tuning, and they were limited in
their ability to handle complex, nonlinear, and dynamic systems [93].
AI techniques have emerged as powerful tools for self-correcting
processing, offering greater adaptability, accuracy, and efficiency compared to traditional methods. Supervised and RL are three major categories of ML that have been utilized in self-correcting techniques in
manufacturing to optimize processes, enhance product quality, and
reduce waste [94]. Supervised learning, where an algorithm learns
to map inputs to outputs based on labeled training data, has been
extensively applied in manufacturing for self-correcting processes [95].
For instance, Cannizzaro et al. [96] employed supervised learning to
develop a predictive model for identifying and correcting defects in additive MP. RL [97], a method where an agent learns to make decisions
by interacting with its environment and receiving feedback in the form
of rewards or penalties, has shown promise in self-correcting techniques
and predictive maintenance in manufacturing [98]. Additionally, RL
Digital twin technology has been gaining popularity in various
industries, including materials science and engineering. A digital twin
is a virtual replica of a physical object or system that can be used to
simulate and predict its behavior in the real world. In materials design
and discovery, digital twins can be used to model and optimize the
properties of materials, predict their behavior under different conditions, and accelerate the discovery of new materials. This chapter will
explore the concept of digital twins in materials design and discovery,
their advantages, and practical applications.
The foundational concepts and frameworks needed to formulate and
continuously update both the form and function of the digital twin
of a selected material physical twin have been established [108]. The
Materials–Information Twin Tetrahedra (MITT) framework captures a
holistic perspective of materials science and engineering in the presence
of modern digital tools and infrastructures [109]. The digital twin
is primarily introduced in Chandhana et al. [110], along with its
advantages and practical applications in different sectors.
Digital twins can provide a range of advantages in materials design
and discovery. They can be used to model and optimize the properties
of materials, predict their behavior under different conditions, and
accelerate the discovery of new materials. Digital twin theory can store
and represent detailed information of objects, such as their materials,
textures, and 2D and 3D images [111]. The concept of digital twins
has already highlighted its potential in industries such as construction,
manufacturing, automotive, and agriculture [112–115].
Digital twins can be used in various practical applications in materials design and discovery. For example, they can be used to simulate
and optimize the properties of materials for specific applications, such
as aerospace or biomedical engineering. They can also be used to
predict the behavior of materials under different conditions, such as
temperature, pressure, or stress. Digital twins can also be used to
accelerate the discovery of new materials by simulating and testing
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with advancements in AI and ML, are expected to contribute to accelerated materials discovery and the development of novel materials for a
wide range of applications. Various types of data are found in materials
science, including experimental, computational, and simulation data.
Experimental data is typically obtained through laboratory testing of
materials, while computational data is generated using mathematical
models and simulations [22]. Simulation data is particularly important,
as it can help predict the properties and behavior of materials under
different conditions, leading to the development of new materials with
desired properties [122].
Several open access materials science databases and repositories are
available and are shown in Table 1.
AI and ML have played a significant role in materials science, particularly in data processing, analysis, and simulation. ML algorithms have
been used to predict material properties, optimize synthesis processes,
and accelerate materials discovery [1,2]. Furthermore, AI techniques
have been employed to analyze large datasets and identify patterns or
correlations that can inform material design [8–10,10,11]. Challenges
in collecting, storing, and accessing materials science data include
data heterogeneity, interoperability, and accessibility. To address these
challenges, efforts have been made to standardize data formats, develop
ontologies for materials science, and promote open access to data.
The future of data in materials science is expected to be shaped by
the continued integration of AI and ML, as well as the development
of new experimental techniques and computational methods. Highthroughput experimentation, which involves the rapid synthesis and
characterization of a large number of materials, will likely generate
vast amounts of data, facilitating the discovery of new materials with
unique properties.
their properties in a virtual environment [116]. The modeling and
implementation method of a digital twin based on a physics simulation
for a material handling system is described in Jeong et al. [117].
Digital twin technology has the potential to revolutionize materials
design and discovery by providing a virtual environment for simulating
and optimizing the properties of materials. The foundational concepts
and frameworks needed to formulate and continuously update both the
form and function of the digital twin of a selected material physical
twin have been established. The advantages and practical applications
of digital twins in materials design and discovery are numerous and
varied. As digital twin technology continues to evolve, it is likely that
its use in materials science and engineering will become increasingly
widespread.
3.4. Characterization and computer vision
CV techniques have been increasingly used in materials design and
manufacturing in recent years due to their ability to process large
amounts of data and identify patterns and features within that data.
These capabilities make them particularly useful for tasks such as
image classification, object recognition, and anomaly detection. In the
field of materials design and manufacturing, CNNs have been applied
to a wide range of applications, including the prediction of material
properties, the optimization of MP, and the identification of defects in
materials [118–121].
One important application of CV in materials design is the prediction of material properties. Accurate predictions of material properties such as strength, ductility and fracture toughness are critical for
the design and development of new materials. CV have been shown
to be effective at predicting these properties based on input data
such as microstructures, compositions, and processing conditions. CNNs
have been used to predict the mechanical properties of metallic alloys [10] based on their microstructure, predict the corrosion resistance
of coatings based on their composition and processing conditions.
Another application of CV techniques in materials design and manufacturing is the optimization of MP. In manufacturing, it is important
to optimize processes in order to improve efficiency, reduce costs, and
improve product quality. CNNs have been used to optimize a variety of
MP, including casting, forging, and welding. For example, CNNs have
been used to predict the final shape of a casting based on the mold
geometry and casting conditions, and to optimize the welding process
in order to minimize defects and improve the strength of the welded
joint.
In addition to these applications, CV techniques have also been
used to identify defects in materials. Defects in materials can have a
significant impact on their performance and durability, and the early
identification of defects is important in order to prevent failures and
improve product quality. CNNs have been used to detect defects in
a variety of materials, including metals, ceramics, and polymers. For
example, CNNs have been used to identify cracks in metal structures,
and to detect defects in ceramic tiles and polymeric materials.
Overall, CV has proven to be a valuable tool in materials design
and manufacturing, with a wide range of applications in the prediction
of material properties, the optimization of MP, and the identification
of defects in materials. As the use of CNNs continues to grow in these
fields, it is likely that CV will play an increasingly important role in the
development of new materials and the optimization of MP.
4.2. Explainability
The proliferation of big data in materials science has opened new avenues for predictive modeling, optimization, and discovery. However,
the sheer volume and complexity of data pose significant challenges.
One of the pressing issues is making sense of intricate models that act
as "black boxes’’, providing predictions without explanations. This has
led to an increasing emphasis on the need for not just robust but also
explainable AI algorithms. Explainability in AI ensures that complex
ML models can be understood, interpreted, and trusted by domain
experts. This is particularly crucial in fields like materials informatics,
where a misinterpretation can lead to suboptimal designs or even safety
hazards [137].
Shapley Analysis based on the SHAP method, originating from
cooperative game theory, have found applications in ML to attribute the
contribution of each feature to the prediction for a particular instance.
In the context of materials science, Shapley Analysis can be employed
to understand how different material properties, influence the overall
performance or suitability of a material for a specific application [138].
A case study is shown in Fig. 5. Shapley values work by approximating
the output of a model with a local linear explanation model. The
coefficients of this explanation model quantify the local effect of each
feature on the output. The coefficients can be aggregated to get global
feature contributions (Fig. 5(a)). Then model is built to relate the
physio-chemical descriptors of a capping layer of lead halide perovskite
solar cells. Afterwards, a ML model is trained to predict the onset time
of degradation of the solar cells under ambient conditions. Shapley
analysis allows the identification of the dominant descriptors in the
model, shown in the figures as a distribution of local Shapley values.
Top polar surface area and H-bond donor have the most significant
impact the output’s prediction and are demonstrated to have dominant
importance by additional experimentation (Fig. 5(b)).
Inverse design stands as another pillar in the realm of explainable
AI in materials science. Traditional material discovery often involves
forward simulations where properties are calculated based on given
4. Big data and explainability
4.1. Data in materials science
Data in materials science plays a critical role in advancing our understanding of material properties and driving innovation in the field.
The availability and accessibility of various types of data, combined
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Table 1
Materials science databases.
Database
Description
Materials Project [123]
A database focused on computational materials science data, including
crystal structures, electronic properties, and thermodynamic data.
Crystallography Open Database (COD) [124]
An open-access repository of crystal structures for various materials.
Inorganic Crystal Structure Database (ICSD) [125]
A database for completely identified inorganic crystal structures.
MatWeb [126]
A comprehensive database of material properties, including mechanical,
thermal, and electrical properties.
Cambridge Structural Database [127]
The Cambridge Structural Database (CSD) is a highly curated and
comprehensive resource. Established in 1965, the CSD is the world’s
repository for small-molecule organic and metal–organic crystal
structures.
International Centre for Diffraction Data [128]
The ICDD is an authority on X-ray powder diffraction and related
diffraction methods. It provides a suite of databases designed for use in
materials identification and characterization.
GDB Databases [129]
The GDB databases are generated by systematic enumeration of all
possible molecules following simple construction rules. These databases
are used for structure prediction, drug discovery, or materials design.
ZINC Database [130]
ZINC is a free database of commercially-available compounds for
virtual screening. It is provided by the Shoichet Laboratory in the
Department of Pharmaceutical Chemistry at the University of
California, San Francisco (UCSF).
Automatic FLOW for Materials Discovery LIBrary [131]
AFLOWLIB is an ab-initio, high-throughput computational materials
repository from Duke University. It is part of the Materials Project, a
multi-institution effort to compute the properties of all known
inorganic materials.
Open Quantum Materials Database [132]
The OQMD is a high-throughput database aimed at the discovery of
new materials. It contains density functional theory (DFT) calculated
properties for millions of materials.
Harvard Clean Energy Project [133]
The Harvard Clean Energy Project used computational chemistry and
the power of distributed computing to find new materials for solar
cells. The project aimed to identify promising organic photovoltaics.
TE Design Lab [134]
The TE Design Lab is a database for thermoelectric materials. It
provides resources for the design and optimization of new
thermoelectric materials.
NREL Materials Database [135]
The National Renewable Energy Laboratory (NREL) Materials Database
is a resource for data on materials used in renewable energy
technologies.
Materials Cloud [136]
Materials Cloud (MC) is a web-based portal for materials science. It
offers educational, research, and archiving tools, and promotes the
sharing and reuse of data generated in the field of materials science.
delve into uncharted territory with confidence and consistency, there
are several challenges that must be addressed.
Firstly, there is the issue of common frameworks to work within
and more specifically, standardization. With regard to the latter, Materials Data Analytics (MODA) and Characterization Data (CHADA) have
emerged as important frameworks. MODA focuses on documenting
materials modeling, encompassing use cases, models, solvers, and other
processing steps. However, it has limitations in its scope, notably a
rigid focus on a static set of physical equations. Work that extends
beyond the predefined theoretical or methodological categories either
has to be forcibly aligned with the closest existing category, or it
cannot be included at all [141]. CHADA, on the other hand, aims
for standardization in material characterization but often results in
representations that lack clear insights into the experiments conducted.
The outcome is a representation that, while understandable to humans,
is frequently ambiguous and often provides less insight than a detailed
research paper explaining the experimental procedures [141]. The lack
of consistent standards extends beyond MODA and CHADA, encompassing data formats, evaluation metrics, and methodologies. This lack of
standardization makes it challenging to compare or integrate different
studies, calling for more adaptable and inclusive standards.
Another issue that arises in the realm of materials science is the
trade-off between model accuracy and interpretability, especially when
employing complex ML models like deep neural networks [137]. These
models are capable of delivering highly precise predictions; however,
material structures. In contrast, inverse design methods work backwards: they start with desired properties and use ML algorithms to
suggest possible material structures or compositions that might exhibit
those properties [139]. Inverse design not only accelerates the material
discovery process but also adds a layer of explainability. By mapping
desired properties back to material compositions or structures, it provides a clear rationale for why a particular material might be suitable
for a specific application [139].
Other methods such as LIME (Local Interpretable Model-agnostic
Explanations) and counterfactual explanations are also gaining traction
in the field of materials informatics. These methods offer localized
explanations for model predictions and can be particularly useful in scenarios where only instance-specific interpretability is required [140].
The need for robust and explainable AI in materials science is not
just a technological requirement but a pragmatic necessity. As the
field continues to be data-rich, the models grow more complex. The
integration of explainability methods like Shapley Analysis and inverse
design serves to make these complex models interpretable, thereby
making the data-driven discovery process in materials science more
transparent, reliable, and actionable.
5. Challenges
While AI techniques have achieved significant strides in material
design, discovery, and process optimization, several areas within materials science remain relatively underexplored. In order for the field to
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I. Papadimitriou et al.
efforts will be key in unlocking the full potential of AI in this rapidly
evolving field.
6. Conclusions
In this comprehensive review, the transformative impact of AI
across various domains of materials science has been highlighted. From
Materials Informatics, Density Functional Theory (DFT), Molecular
Dynamics (MD), to Finite Elements Analysis (FEA), the breadth and
depth of AI’s applicability in materials science have been demonstrated.
Beyond the scope of material design, the role of AI in revolutionizing
manufacturing through process optimization techniques is discussed.
Active learning and self-correcting systems are shown to represent
a new era in smart manufacturing. Additionally, Digital Twins are
presented as a groundbreaking tool for simulation and optimization
in industrial settings. The necessity for both explainable and robust
AI systems is emphasized, thereby fulfilling a critical requirement for
the broader adoption of AI in materials science. This focus aligns
with the discussion on Big Data, where the potential of data-driven
methodologies is illuminated.
However, this review also identifies several challenges and gaps that
exist in the current landscape of AI applications in materials science.
These range from the urgent need for standardization, highlighted by
initiatives like MODA and CHADA, to the trade-offs between model
accuracy and interpretability. Questions surrounding the computational
costs of AI algorithms are raised, pointing to concerns about scalability
and environmental impact. The democratization of AI and the importance of open data are also discussed as critical factors for the future
development of the field.
In conclusion, as AI technologies continue to evolve and find deeper
integration into materials science, addressing these multifaceted challenges through open dialogue, rigorous scientific scrutiny, and collaborative research is imperative. Such a comprehensive approach is
expected to mitigate existing challenges and unlock new avenues for
discovery and innovation in materials science.
Fig. 5. Interpretability with Shapley values. (a) Shapley values are a generalization of
various black-box explainability methods. (b) Case study of Shapley analysis in material
science.
Source: The image is reproduced under the CC-BY 4.0 License from Ref. [137].
their intricate architecture often acts as a "black box’’, making it
difficult to understand the underlying mechanisms that led to a particular decision. In materials science, this lack of interpretability can
be a significant drawback. Understanding the ‘why’ and ‘how’ behind
predictions is essential for gaining insights into material properties and
behaviors, which in turn is crucial for the iterative process of material
design and discovery.
Computational efficiency is another critical challenge in applying
AI to materials science. Advanced algorithms, especially deep learning models, often require significant computational resources for both
training and inference. This resource intensity raises serious concerns
about the scalability of these techniques. Specifically, it calls into question their applicability in real-time scenarios, where quick decisionmaking is essential, or in settings where computational resources are
constrained. The computational burden not only limits the immediate
utility of these algorithms but also poses long-term questions about the
energy efficiency and environmental impact of large-scale deployments.
Another aspect worth considering is the democratization of AI in
the field of materials science. The advent of open-source platforms
and community-driven resources is lowering the entry barrier, making
it possible for a wider range of researchers, including those from
smaller institutions, to engage in cutting-edge research [123]. This
democratization has the potential not only to accelerate innovation but
also to diversify the sources of breakthroughs in the field. Rather than
waiting for advancements to trickle down from a few well-resourced
organizations with extensive data centers, democratization allows for
a more collective, widespread contribution to the discovery and design
of new materials. The same goes for open access to high-quality, standardized datasets not only fosters collaboration but also enables more
robust ML models [123]. As AI continues to advance, the importance of
open data becomes even more critical for validating models, ensuring
quality control, and accelerating innovation in the field.
Addressing these multifaceted challenges and opportunities is crucial for the sustainable and responsible advancement of AI in materials
science. Open dialogue, rigorous scientific scrutiny, and collaborative
CRediT authorship contribution statement
Ioannis Papadimitriou: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review &
editing. Ilias Gialampoukidis: Writing – review & editing. Stefanos
Vrochidis: Resources. Ioannis Kompatsiaris: Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgment
This work was supported by the Horizon Europe Framework Programme and EC-funded project DiMAT, EU under grant agreement No
101091496.
Appendix
See Table 2.
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I. Papadimitriou et al.
Table 2
Table of acronyms in order of appearance.
Acronym
Full form
AI
CV
CNN
NLP
ML
IoT
MP
DL
GAN
PINN
PDE
SVM
KNN
EA
NLP
DFT
MD
PAW
GAN
NNP
FEA
MODA
CHADA
RL
PCA
GMM
ANN
DT
GB
DBSCAN
RNN
GPT-3
SQL
Cobots
SPC
DoE
PID
RL
MITT
XAI
COD
ICSD
CSD
ICDD
GDB
UCSF
AFLOWLIB
OQMD
HCEP
TE
NREL
MC
XAI
Artificial Intelligence
Computer Vision
Convolutional Neural Network
Natural Language Processing
Machine Learning
Internet of Things
Manufacturing Processes
Deep Learning
Generative Adversarial Network
Physics-Informed Neural Network
Partial Differential Equations
Support Vector Machines
K-Nearest-Neighbors
Evolutionary Algorithms
Natural Language Processing
Density Functional Theory
Molecular Dynamics
Projector-augmented Wave
Generative Adversarial Network
Neural Network Potential
Finite Element Analysis
Materials Data Analytics
Characterization Data
Reinforcement Learning
Principal Component Analysis
Gaussian Mixture Model
Artificial Neural Network
Decision Trees
Gradient Boosting
Density-Based Spatial Clustering of Applications with Noise
Recurrent Neural Network
Generative Pretrained Transformer 3
Structured Query Language
Collaborative Robots
Statistical Process Control
Design of Experiments
Proportional-Integral-Derivative
Reinforcement Learning
Materials–Information Twin Tetrahedra
Explainable AI
Crystallography Open Database
Inorganic Crystal Structure Database
Cambridge Structural Database
International Centre for Diffraction Data
Generated Database
University of California, San Francisco
Automatic FLOW for Materials Discovery LIBrary
Open Quantum Materials Database
Harvard Clean Energy Project
ThermoElectric
National Renewable Energy Laboratory
Materials Cloud
Explainable AI
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