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Front. Phys.    2024, Vol. 19 Issue (1) : 13501    https://doi.org/10.1007/s11467-023-1325-z
REVIEW ARTICLE
Advances of machine learning in materials science: Ideas and techniques
Sue Sin Chong1, Yi Sheng Ng1, Hui-Qiong Wang1,2(), Jin-Cheng Zheng1,2()
1. Department of New Energy Science and Engineering, Xiamen University Malaysia, Sepang 43900, Malaysia
2. Engineering Research Center of Micro-nano Optoelectronic Materials and Devices, Ministry of Education; Fujian Key Laboratory of Semiconductor Materials and Applications, CI Center for OSED, and Department of Physics, Xiamen University, Xiamen 361005, China
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Abstract

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.
Keywords machine learning      materials science     
Corresponding Author(s): Hui-Qiong Wang,Jin-Cheng Zheng   
Online First Date: 14 September 2023    Issue Date: 14 September 2023
 Cite this article:   
Sue Sin Chong,Yi Sheng Ng,Hui-Qiong Wang, et al. Advances of machine learning in materials science: Ideas and techniques[J]. Front. Phys. , 2024, 19(1): 13501.
 URL:  
https://academic.hep.com.cn/fop/EN/10.1007/s11467-023-1325-z
https://academic.hep.com.cn/fop/EN/Y2024/V19/I1/13501
Fig.1  List of the conventional machine learning tasks and the problems tackled [68].
Fig.2  List of typical machine learning terminologies [68].
Fig.3  Illustration of the typical stages of a learning process [68].
Ideas & technique Relevant development and models
Pre-training Ref. [71], Ref. [74], BLIP [75], Pretrained transformers [76], Ref. [77]
Fine-tuning Ref. [78], Ref. [79]
Bidirectional encoder BERT [80], Albert [81], Robustly optimized BERT pre-training approach (RoBERTa) [82], CodeBERT [72], BeiT [73]
Transformer Ref. [69], Ref. [83], Ref. [84], Transfomer memory as search index [85]
Attention prompt Ref. [86], AutoPrompt [87], OpenPrompt [88]
Learning extra huge models Open pretrained transfomer (OPT 175B) [89], Jurassic-1 [90], Generative pre-trained transformer 3 (GPT-3) [91], CLD-3 [92]
End-to-end model Word2Vec [93], Global vectors for word representation (GLoVE) [94], Context2Vec [95], Structure2Vec [96], Driver2Vec [97], wav2Vec [98]
Tab.1  Natural language processing (NLP) ideas, techniques and models.
Ideas & techniques Relevant literature
Visual models Visual transformer [101], Flamingo (LM) [102]
Image-text processing CoCa [103], FuseDream [104], CLIP [105]
Convolutional neural network DEtection transformer (DETR)[106], LiT [107], Ref. [108]
Image rendering Dall-E [109], Review [110], Neural radiance field (NeRF) [111]
Point cloud reconstruction PointNorm [112], Ref. [113], Residual MLP [114], Learning on point cloud [115]
Tab.2  Computer vision (CV) ideas, techniques and references.
Ideas & techniques Relevant literature
Types of RL Q-learning [119], SARSA [118], Temporal difference (TD)-learning [123]
RL algorithm Self-training [124], Deep Q-learning (DQN) [125], Deep deterministic policy gradient (DDPG) [126], Offline [127]
Apprenticeship learning efficient RL SayCan[128], Q-attention [129], Imitation learning [130] Replay with compression [131], Decision transformer [132]
Evolving curriculum Adversarially compounding complexity by editing levels (ACCEL) [133], Paired open-ended trailblazer (POET) [134], Autonomous driving scene render (READ) [135]
Bandit problem Bandit learning [136], Batched bandit [137], Dueling bandit [138], Upper confidence bound (UCB) [139]
Tab.3  Reinforcement learning (RL) ideas, techniques and references.
Fig.4  Feature engineering for ML applications. (a) Feature extraction process. Starting from material space, one can extract information from material space into chemical structures then to descriptors space. (b) Typical ML feature analysis methods. “FEWD” refers to Filter method, Embedded method, Wrapper method, and Deep learning. (c) Correlation and importance analysis of selected features. The feature correlations is visualized in the diagram on the left. Diagram on the right is normalized version of left diagram, where the colors indicate the relative correlation of every other feature for prediction of the row/column feature. (d) Various feature subsets obtained from feature engineering analysis. One can construct features with linearly independent combination of subsets, in other words, subsets of features are basis. Reproduced with permission from Ref. [172].
Fig.5  Infographic of End-to-End Model. End-to-End models take multi-modal dataset as inputs, and encodes them into vectors for the surrogate model. The surrogate model then learns the latent representation, which makes the internal patterns of these datasets indexable. One is then able to decode the latent representation into an output form of our choice, which includes property predictions, generated novel materials and co-pilot simulation engines.
Fig.6  Schematic of the representation learning methods used in the structural characterization of catalysts, where the autoencoder, which includes the encoder and decoder, is used, with the input and output data being the same. Reproduced with permission from Ref. [176].
Fig.7  Depending on the degree of freedom (DOF) involved, the machine learning methodologies of the photonic design vary. The analytical methods that are suitable for DOF of order unity are replaced by the discriminative model of ML. As DOF increases, generative model is leveraged to bring down the dimensionality. Reproduced with permission from Ref. [178].
Data type Database
Computational data OQMD: Materials properties calculated from DFT [179,180], Materials project [181], Joint automated repository for various integrated simulations (JARVIS) [182], AFLOW [183], MatCloud [184], MPDS [185], NOMAD [186], C2DB [187], 2DMatPedia [188]
Crystallographic data ICSD [189], Crystallography open database (COD) [190], The NIST surface structure database (SSD4) [41], Aspherical electron scattering factors [191], AlphaFold [192]
Imaging/spectra data MatBench [193], TEMImageNet [194], Single-atom library [195]
Other types Knowledge graph, e.g., propnet [196]
Tab.4  Typical material science databases.
Library Library Description
General deep learning libraries (APIs) Deepmind Jax [201] Open ML codebase by Deepmind. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy code.
Keras [202] Free open source Python library for developing and evaluating deep learning models.
PyTorch [203] PyTorch is an open source machine learning framework based on the Torch library.
TensorFlow [204] Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning.
Useful libraries for machine learning tasks HuggingFace [205] Open NLP Library with Trained Models, API and Dataset Loaders.
OpenRefine [206] OpenRefine is an open-source desktop application for data cleanup and transformation to other formats, an activity commonly known as data wrangling.
PyTorch Geometric [207] PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
PyTorch lightning [208] PyTorch lightning is the deep learning framework for professional AI researchers and machine learning engineers who need maximal flexibility without sacrificing performance at scale.
VectorFlow [209] Optimized for sparse data in single machine environment.
Weights & Biases [210] W&B for experiment tracking, dataset versioning, and collaborating on ML projects.
Tools that might be useful to material science Dscribe [211] Provides popular feature transformations (“descriptors”) for atomistic materials simulations, including Coulomb matrix, Ewald sum matrix, sine matrix, Many-body tensor representation (MBTR), Atom-centered symmetry funsction (ACSF) and Smooth overlap of atomic positions (SOAP).
Open graph database [212] The open graph benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader.
RDKit [213] Opensource library for converting molecules to SMILES string.
Spektral [214] Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2.
Tab.5  Machine learning libraries. All descriptions were adapted from the references therein.
Fig.8  (a) The mathematical description of the Weyl and Coulomb matrices. (b) The construction of the PRDF sums, where atoms covered by the yellow strip covering the radius (r,r +dr) are considered. (b) Reproduced with permission from Ref. [221].
Fig.9  (a) Structure graph for 2,3,4-trimethylhexane and (b) the related adjacency and distance matrix. Reproduced with permission from Ref. [231]. (c) The Universal fragment descriptors. The crystal structure is analysed for atomic neighbours via Voronoi tessellation with the infinite periodicity taken into account. Reproduced with permission from Ref. [232].
Fig.10  Crystal graph construction proposed used in the generalized crystal graph convolutional neural networks. Reproduced with permission from Ref. [233].
Fig.11  (a) Left to right: 0-, 1-, 2-, 3-simplex. (b) An example of a simplicial complex, with five vertices: a, b, c, d, and e, six 1-simplices: A, B, C, D, E, and F, and one 2-simplex T. The Betti numbers for this complex are β0=β1=1. Reproduced with permission from Ref. [238].
Fig.12  Persistence barcode plot for the selected Na atom inside a NaCl crystal, surrounded by only (a) Na atoms and (b) Cl atoms. (c) Construction of crystal topological descriptor, taking into account different chemical environmen t. Reproduced with permission from Ref. [236].
Fig.13  (a) Experimental XRD method, where X-ray plane wave incidents on a crystal, resulting in diffraction fingerprints. (b, c) XRD-based image descriptor for a crystal where each RGB colour corresponds to rotation about the x, y, z axes. The robustness of the descriptor against defects can be observed by comparing (b) to (c). (d) Examples of 1D XRD. (a−c) Reproduced with permission from Ref. [239], (d) Reproduced with permission from Ref. [241].
ML algorithms Tool
Support vector machine (SVM) Refs. [260, 261, 262, 246]
Kernel ridge regression (KRR) Refs. [237, 263, 247, 264]
Deep neural network VampNet [257], DTNN [265], ElemNet [266], IrNet [267], PhysNet [268], DeepMolNet [269], SIPFENN [270], SpookyNet [250]
Convolutional neural network (CNN) SchNet [271], Refs. [239, 240, 272, 273]
Graph neural network (GNN) CGCNN [274], MEGNet [275], GATGNN [276], OrbNet [277], DimeNET [278], ALIGNN [279], MXMNet [280], GraphVAMPNet [281], GdyNets [282], NequIP [283], PaiNN [284], CCCGN [285, 286], FFiNet [287]
Generative adversarial networks (GAN) Ref. [288], CrystalGAN [246], MatGAN [289]
Variational auto encoder (VAE) FTCP [290], CDVAE [291], Refs. [292, 263]
Random forest/ decision tree Refs. [236, 293, 294, 251, 295, 296]
Unsupervised clustering Refs. [241, 282, 252, 297, 298]
Transfer learning Roost [299], AtomSets [288], XenonPy.MDL [289], TDL [290], Refs. [256, 291, 292, 300, 301]
Tab.6  List of Machine Learning (ML) algorithms used by various tools or framework developed in materials science.
Fig.14  (a) Neural network (NN) with 3 layers: input, hidden, and output. (b) Deep NN with 3 hidden layers. Reproduced with permission from Ref. [257].
Fig.15  The CNN architecture used in the work of Ziletti et al. [239]. (a) A kernel or learnable filter is applied all over the image, taking scalar product between the filter and the image data at every point, resulting in an activation map. This process is repeated in (b), which is then coarse grained in (c), reducing the dimension. The map is then transferred to regular NNs hidden layers (d) before it is used to classify the crystal structure (e). Reproduced with permission from Ref. [239].
Fig.16  (a) An example of a decision tree, where each square represents internal node or feature, each arrow represents branch or decision rule, and the green circles are leafs representing class labels or numerical values. (b) Dendogram obtained via agglomerative hierarchical clustering (AHC) where the dashed line indicates the optimal clustering. (a) Reproduced with permission from Ref. [258], (b) Reproduced with permission from Ref. [241].
Fig.17  The architectures of the two generative models, Generative adversarial networks (GAN) and Variational auto encoders (VAE). Reproduced with permission from Ref. [259].
Fig.18  (a) Composition-conditioned crystal GAN, designed to generate crystals that can be applied in photodiode. (b) Simplified VAE architecture used in the inverse design of VxOy materials. (a) Reproduced with permission from Ref. [253], (b) Reproduced with permission from Ref. [254].
Fig.19  (a) Overview of explainable DNNs approaches. (b) Feature visualization in the form of heat map used in determining the ionic conductivity from SEM images. (a) Reproduced with permission from Ref. [302], (b) Reproduced with permission from Ref. [305].
Fig.20  (a) The architecture of CrysXPP, which is capable of producing explainable results, as seen in (b) the bar chart of features affecting the band gap of GaP crystal. Reproduced with permission from Ref. [306].
Fig.21  High-throughput screening with learnt interatomic potential embedding from Ref. [330]. With the integration of active learning and DFT in the screening pipeline, the throughput efficiency or the quality of the output obtained from calculation can be improved. Reproduced with permission from Ref. [330].
Fig.22  Schematics of generative adversarial network. Reproduced with permission from Ref. [332].
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