Explainable Machine Learning in the Research of Materials Science

doi:10.11900/0412.1961.2024.00160

Acta Metall Sin

2024, Vol. 60

Issue (10): 1345-1361 DOI: 10.11900/0412.1961.2024.00160

Overview

Current Issue | Archive | Adv Search

Explainable Machine Learning in the Research of Materials Science

WANG Guanjie, LIU Shengxian, ZHOU Jian, SUN Zhimei(

)

School of Materials Science and Engineering, Beihang University, Beijing 100191, China

Cite this article:

WANG Guanjie, LIU Shengxian, ZHOU Jian, SUN Zhimei. Explainable Machine Learning in the Research of Materials Science. Acta Metall Sin, 2024, 60(10): 1345-1361.

Download:

HTML

PDF(2959KB)
Export: BibTeX | EndNote (RIS)

Abstract

With the rapid advancement of artificial intelligence (AI), machine learning is playing an increasingly important role in materials research, development, and design. Traditional machine learning models are often “black box” models that limit researchers' understanding of a model's decision-making and undermines their confidence in the process. Explainable machine learning (XML) can reveal the internal mechanisms of these models and provide insights into their decision-making processes. This study begins with the fundamentals of XML, outlines the development history and notable milestones of XML methods, and discusses the role of XML in AI, emphasizing the Fairness, Accountability, Simplicity, and Transparency (F.A.S.T.) principles that should be followed. Furthermore, this study introduces two major categories of XML methods—those that use model-intrinsic interpretability and those that use external model interpretability—along with their applications in materials science. Specifically, the symbolic regression of XML and visualized XML methods developed by our team offer new tools for materials research and design. Finally, potential directions for XML in the field of materials science are discussed.

Key words: explainable machine learning materials genome engineering symbolic regression

Received: 13 May 2024

ZTFLH:

TB30

Fund: National Key Research and Development Program of China(2022YFB3807200)

Corresponding Authors: SUN Zhimei, professor, Tel: (010)82317747, E-mail: zmsun@buaa.edu.cn

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors
	WANG Guanjie
	LIU Shengxian
	ZHOU Jian
	SUN Zhimei

URL:

https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00160 OR https://www.ams.org.cn/EN/Y2024/V60/I10/1345

Fig.1 Development history of explainable machine learning (XML) (based on the number of SCI papers published with the keyword “Explainable Machine Learning” retrieved from the Web of Science database)

Fig.2 Aim and concept of America Defense Advanced Research Projects Agency's explainable artificial intelligence (DARPA's XAI) (a), and the positioning of XML in the field of artificial intelligence (AI)^[69] (b)

Fig.3 Classification of XML methods

Fig.4 Continuous decision tree search algorithm flow (The spheres represent candidates for different model parameters in the MCTS run, and the numbers on the spheres correspond to their node positions in the Monte Carlo Tree Search (MCTS) tree shown in Fig.4b. These numbers roughly correspond to the exploration order of the candidates, Δ represents the difference between predicted values and objective values) (a); schematic of the root node, parent node, child nodes and their relationships in the MCTS decision tree structure (b); discrete data points in the search space of the traditional MCTS algorithm (c); and parameter search problem in this paper so that the algorithm can converge to the optimal solution (d)^[83]

Fig.5 Training process of K nearest neighbor algorithm in material classification problem^[86] (M—martensite, F—ferrite, B—bainite, RA—retained austenite, C—carbide, K-NN—K nearest neighbors, SMOTE—synthetic minority overbalancing technique)

Fig.6 Schematic of backpropagation in layer-wise relevance propagation (LRP) method ( R_i —relevance score of the input feature, R_j —relevance score of the feature in an intermediate layer, R_k —relevance score of the feature in the output layer, $R j ← k$ —weight in back propagation)

Fig.7 Three-dimensional structure of the protein-ligand complex, 3F7H (The protein is depicted in a cartoon (green), and the ligand is depicted in color-coded ball-and-stick. Atom colors: gray (C), red (O), and blue (N)) (a); knowledge-based estimation of protein-ligand interactions (Hydrogen bonds are depicted in red dashed lines, and hydrophobic contacts are depicted in gray dashed lines) (b); heat map for the atomic contributions on the prediction of interaction, obtained from the LRP on PotentialNet (c); and heat map for the atomic contributions on the prediction of interaction, obtained from the LRP on InteractionNet (d) (The contributions are illustrated with color intensity of red (positive influence), white (zero influence), and blue (negative influence) colors)^[96]

Fig.8 Examples of hypercolumn representations for eight image classes: cluster (a), fiber (b), matrix (c), matrix-surface (d), hraphene sheets (e), soot particles (f), high-density particles (g), and polymer residuals (h); Schematic of class activation map (CAM) in image classification problem^[98] (Conv Block—convolutional neural network unit. FC Block—fully connected unit) (i)

Table 1 Evaluation results of XML methods

Fig.9 Prediction model for oxygen evolution reaction (OER) (a) and oxygen reduction reaction (ORR) (b) reactivity of transition metal boride (MBene) single-atom catalysts based on interpretable symbolic regression (η^ORR and η^OER—efficiencies of ORR or OER, respectively; MSE—squared error; MAE—mean absolute error; RMSE—root mean squared error; DFT—density functional theory)^[103]

Fig.10 Visualized machine learning in ALKEMIE multi-scale high-throughput computing and data management intelligent platform

1	Batra R. Machine learning from diverse data sources [J]. Nature, 2021, 589: 524
2	Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects [J]. Science, 2015, 349: 255 doi: 10.1126/science.aaa8415 pmid: 26185243
3	Poggio T, Torre V, Koch C. Computational vision and regularization theory [A]. Readings in Computer Vision: Issues, Problems, Principles, and Paradigms [M]. San Francisco: Morgan Kaufmann Publishers Inc., 1987: 638
4	Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions [A]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [C]. Boston: IEEE, 2015: 1
5	He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [A]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [C]. Las Vegas: IEEE, 2016: 770
6	Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection [A]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [C]. Las Vegas: IEEE, 2016: 779
7	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [J]. Commun. ACM, 2017, 60: 84
8	Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: A brief review [J]. Comput. Intell. Neurosci., 2018, 2018: 7068349
9	Deng L, Li X. Machine learning paradigms for speech recognition: An overview [J]. IEEE Trans. Audio Speech Lang. Process., 2013, 21: 1060
10	Afouras T, Chung J S, Senior A, et al. Deep audio-visual speech recognition [J]. IEEE Trans. Pattern Anal. Mach. Intell., 2018, 44: 8717
11	Kong Q Q, Cao Y, Iqbal T, et al. PANNs: Large-scale pretrained audio neural networks for audio pattern recognition [J]. IEEE/ACM Trans. Audio Speech Lang. Process., 2020, 28: 2880
12	Cambria E, White B. Jumping NLP curves: A review of natural language processing research [J]. IEEE Comput. Intell. Mag., 2014, 9: 48
13	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [A]. Proceedings of the 31st International Conference on Neural Information Processing Systems [C]. Long Beach: Curran Associates Inc., 2017: 6000
14	Liang W X, Tadesse G A, Ho D, et al. Advances, challenges and opportunities in creating data for trustworthy AI [J]. Nat. Mach. Intell., 2022, 4: 669
15	LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series [A]. The Handbook of Brain Theory and Neural Networks [M]. Cambridge: MIT Press, 1995: 3361
16	LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521: 436
17	Salakhutdinov R. Learning deep generative models [J]. Annu. Rev. Stat. Appl., 2015, 2: 361
18	Stevens R, Taylor V, Nichols J, et al. AI for science: Report on the department of energy (DOE) town halls on artificial intelligence (AI) for science [R]. Argonne: Argonne National Lab, 2020
19	Raccuglia P, Elbert K C, Adler P D F, et al. Machine-learning-assisted materials discovery using failed experiments [J]. Nature, 2016, 533: 73
20	Bradlyn B. Data mining uncovers a treasure trove of topological materials [J]. Nature, 2019, 566: 425
21	Lu H Y, Diaz D J, Czarnecki N J, et al. Machine learning-aided engineering of hydrolases for pet depolymerization [J]. Nature, 2022, 604: 662
22	Rao Z Y, Tung P Y, Xie R W, et al. Machine learning-enabled high-entropy alloy discovery [J]. Science, 2022, 378: 78 doi: 10.1126/science.abo4940 pmid: 36201584
23	Curtarolo S, Hart G L W, Nardelli M B, et al. The high-throughput highway to computational materials design [J]. Nat. Mater., 2013, 12: 191 doi: 10.1038/nmat3568 pmid: 23422720
24	Setyawan W, Curtarolo S. High-throughput electronic band structure calculations: Challenges and tools [J]. Comput. Mater. Sci., 2010, 49: 299
25	Xu D G, Zhang Q, Huo X Y, et al. Advances in data‐assisted high‐throughput computations for material design [J]. Mater. Genome Eng. Adv., 2023, 1: e11
26	Xiang X D, Sun X D, Briceño G, et al. A combinatorial approach to materials discovery [J]. Science, 1995, 268: 1738 pmid: 17834993
27	Huxtable S, Cahill D G, Fauconnier V, et al. Thermal conductivity imaging at micrometre-scale resolution for combinatorial studies of materials [J]. Nat. Mater., 2004, 3: 298 pmid: 15064757
28	Wang Z, Sun Z H, Yin H, et al. Data‐driven materials innovation and applications [J]. Adv. Mater., 2022, 34: 2104113
29	Su Y J, Fu H D, Bai Y, et al. Progress in materials genome engineering in China [J]. Acta Metall. Sin., 2020, 56: 1313 doi: 10.11900/0412.1961.2020.00199
	宿彦京, 付华栋, 白洋等. 中国材料基因工程研究进展 [J]. 金属学报, 2020, 56: 1313 doi: 10.11900/0412.1961.2020.00199
30	Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4: 11
31	Liu Z K. Perspective on materials genome [J]. Chin. Sci. Bull., 2014, 59: 1619
32	O'Mara J, Meredig B, Michel K. Materials data infrastructure: A case study of the Citrination platform to examine data import, storage, and access [J]. JOM, 2016, 68: 2031
33	Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: A review on methods and applications of machine learning for materials design [J]. Engineering, 2019, 5: 1017
34	Liu Z K. View and comments on the data ecosystem: “Ocean of data” [J]. Engineering, 2020, 6: 604
35	Zhu L G, Zhou J, Sun Z M. Materials data toward machine learning: Advances and challenges [J]. J. Phys. Chem. Lett., 2022, 13: 3965
36	Zhang H T, Fu H D, He X Q, et al. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening [J]. Acta Mater., 2020, 200: 803
37	Zhang H T, Fu H D, Zhu S C, et al. Machine learning assisted composition effective design for precipitation strengthened copper alloys [J]. Acta Mater., 2021, 215: 117118
38	Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357
	谢建新, 宿彦京, 薛德祯等. 机器学习在材料研发中的应用 [J]. 金属学报, 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357
39	Wang C X, Zhang Y, Wen C, et al. Symbolic regression in materials science via dimension-synchronous-computation [J]. J. Mater. Sci. Technol., 2022, 122: 77 doi: 10.1016/j.jmst.2021.12.052
40	Fan Z Y, Song K K, Zhao R, et al. General-purpose machine-learned potential for 16 elemental metals and their alloys [J]. arXiv: 2311. 04732, 2023
41	Liu X F, Zhang Y F, Wang W T, et al. Transition metal and n doping on alp monolayers for bifunctional oxygen electrocatalysts: Density functional theory study assisted by machine learning description [J]. ACS Appl. Mater. Interfaces, 2022, 14: 1249
42	Zhi H H, Ma Z X, Chen L, et al. Hydrogen-promoted heterogeneous plastic strain and associated hardening effect in polycrystalline nickel under uniaxial tension [J]. Mater. Sci. Eng., 2024, A894: 146190
43	Schmidt M, Lipson H. Distilling free-form natural laws from experimental data [J]. Science, 2009, 324: 81 doi: 10.1126/science.1165893 pmid: 19342586
44	O'Connor N J, Jonayat A S M, Janik M J, et al. Interaction trends between single metal atoms and oxide supports identified with density functional theory and statistical learning [J]. Nat. Catal., 2018, 1: 531
45	Xiong J, Zhang T Y, Shi S Q. Machine learning of mechanical properties of steels [J]. Sci. China Technol. Sci., 2020, 63: 1247
46	Weng B C, Song Z L, Zhu R L, et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts [J]. Nat. Commun., 2020, 11: 3513 doi: 10.1038/s41467-020-17263-9 pmid: 32665539
47	Foppa L, Purcell T A R, Levchenko S V, et al. Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites [J]. Phys. Rev. Lett., 2022, 129: 055301
48	Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering [J]. Science, 2018, 361: 360 doi: 10.1126/science.aat2663 pmid: 30049875
49	Wang C S, Fu H D, Jiang L, et al. A property-oriented design strategy for high performance copper alloys via machine learning [J]. npj Comput. Mater., 2019, 5: 87
50	Zhao Y, Siriwardane E M D, Wu Z Y, et al. Physics guided deep learning for generative design of crystal materials with symmetry constraints [J]. npj Comput. Mater., 2023, 9: 38
51	Kim E, Huang K, Jegelka S, et al. Virtual screening of inorganic materials synthesis parameters with deep learning [J]. npj Comput. Mater., 2017, 3: 53
52	Segler M H S, Preuss M, Waller M P. Planning chemical syntheses with deep neural networks and symbolic AI [J]. Nature, 2018, 555: 604
53	Aykol M, Hegde V I, Hung L, et al. Network analysis of synthesizable materials discovery [J]. Nat. Commun., 2019, 10: 2018 doi: 10.1038/s41467-019-10030-5 pmid: 31043603
54	Shields B J, Stevens J, Li J, et al. Bayesian reaction optimization as a tool for chemical synthesis [J]. Nature, 2021, 590: 89
55	Szczypiński F T, Bennett S, Jelfs K E. Can we predict materials that can be synthesised? [J]. Chem. Sci., 2021, 12: 830
56	Tao H C, Wu T Y, Aldeghi M, et al. Nanoparticle synthesis assisted by machine learning [J]. Nat. Rev. Mater., 2021, 6: 701
57	Lindsey R K, Pham C H, Goldman N, et al. Machine-learning a solution for reactive atomistic simulations of energetic materials [J]. Propellants Explos. Pyrotech., 2022, 42: e202200001
58	Castelvecchi D. Can we open the black box of AI? [J]. Nature, 2016, 538: 20
59	Papernot N, McDaniel P, Goodfellow I, et al. Practical black-box attacks against machine learning [A]. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security [C]. Abu Dhabi: Association for Computing Machinery, 2017: 506
60	Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead [J]. Nat. Mach. Intell., 2019, 1: 206 doi: 10.1038/s42256-019-0048-x pmid: 35603010
61	Papernot N, McDaniel P, Goodfellow I. Transferability in machine learning: From phenomena to black-box attacks using adversarial samples [J]. arXiv:1605. 07277, 2016
62	Koh P W, Liang P. Understanding black-box predictions via influence functions [A]. Proceedings of the 34th International Conference on Machine Learning [C]. Sydney: JMLR.org, 2017: 1885
63	Petch J, Di S, Nelson W. Opening the black box: The promise and limitations of explainable machine learning in cardiology [J]. Can. J. Cardiol., 2022, 38: 204
64	Burkart N, Huber M F. A survey on the explainability of supervised machine learning [J]. J. Artif. Intell. Res., 2021, 70: 245
65	Molnar C. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable [M]. Munich, Germany, 2022: 318 (Independently published)
66	Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations [J]. J. Comput. Phys., 2019, 378: 686 doi: 10.1016/j.jcp.2018.10.045
67	Tshitoyan V, Dagdelen J, Weston L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature [J]. Nature, 2019, 571: 95
68	Karniadakis G E, Kevrekidis I G, Lu L, et al. Physics-informed machine learning [J]. Nat. Rev. Phys., 2021, 3: 422 doi: 10.1038/s42254-021-00314-5
69	Gianfagna L, Di Cecco A. Explainable AI with Python [M]. Cham: Springer, 2021: 6
70	Utgoff P E. ID5: An incremental ID3 [A]. Proceedings of the Fifth International Conference on Machine Learning [C]. Ann Arbor: University of Michigan, 1988: 107
71	Utgoff P E. Incremental induction of decision trees [J]. Mach. Learn., 1989, 4: 161
72	Quinlan J R. Improved use of continuous attributes in C4.5 [J]. J. Artif. Intell. Res., 1996, 4: 77
73	Hssina B, Merbouha A, Ezzikouri H, et al. A comparative study of decision tree ID3 and C4.5 [J]. Int. J. Adv. Comput. Sci. Appl., 2014, 4: 13
74	Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors [J]. Nature, 1986, 323: 533
75	Roscher R, Bohn B, Duarte M F, et al. Explainable machine learning for scientific insights and discoveries [J]. IEEE Access, 2020, 8: 42200
76	Raabe D, Mianroodi J R, Neugebauer J. Accelerating the design of compositionally complex materials via physics-informed artificial intelligence [J]. Nat. Comput. Sci., 2023, 3: 198 doi: 10.1038/s43588-023-00412-7 pmid: 38177883
77	Ribeiro M T, Singh S, Guestrin C. “Why should I trust you?” Explaining the predictions of any classifier [A]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining [C]. San Francisco: Association for Computing Machinery, 2016: 1135
78	Lundberg S M, Lee S I. A unified approach to interpreting model predictions [A]. Proceedings of the 31st International Conference on Neural Information Processing Systems [C]. Long Beach: Curran Associates Inc., 2017: 4768
79	Gunning D, Vorm E, Wang J Y, et al. DARPA's explainable AI (XAI) program: A retrospective [J]. Appl. AI Lett., 2021, 2: e61
80	Doreswamy H. Linear regression model for knowledge discovery in engineering materials [A]. Proceedings of the First International Conference on Artificial Intelligence, Soft Computing and Applications [C]. London: Computer Science & Information Technology, 2011: 147
81	Pregibon D. Logistic regression diagnostics [J]. Ann. Statist., 1981, 9: 705
82	Song Y Y, Lu Y. Decision tree methods: Applications for classification and prediction [J]. Shanghai Arch. Psychiatry, 2015, 27: 130
83	Manna S, Loeffler T D, Batra R, et al. Learning in continuous action space for developing high dimensional potential energy models [J]. Nat. Commun., 2022, 13: 368 doi: 10.1038/s41467-021-27849-6 pmid: 35042872
84	Wang G J, Wang E P, Li Z F, et al. Exploring the mathematic equations behind the materials science data using interpretable symbolic regression [J]. Interdiscip. Mater., 2024: 1, doi: 10.1002/idm2.12180
85	Peterson L E. K-nearest neighbor [J]. Scholarpedia, 2009, 4: 1883
86	Gupta A K, Chakroborty S, Ghosh S K, et al. A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm [J]. Comput. Mater. Sci., 2023, 228: 112321
87	Toloşi L, Lengauer T. Classification with correlated features: Unreliability of feature ranking and solutions [J]. Bioinformatics, 2011, 27: 1986 doi: 10.1093/bioinformatics/btr300 pmid: 21576180
88	Greenwell B M. PDP: An R package for constructing partial dependence plots [J]. R J., 2017, 9: 421
89	Mangalathu S, Hwang S H, Jeon J S. Failure mode and effects analysis of RC members based on machine-learning-based Shapley additive explanations (SHAP) approach [J]. Eng. Struct., 2020, 219: 110927
90	Yang J L. Fast TreeSHAP: Accelerating SHAP value computation for trees [J]. arXiv: 2109. 09847, 2021
91	Covert I, Lee S I. Improving KernelSHAP: Practical Shapley value estimation using linear regression [A]. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics [C]. San Diego: PMLR, 2021: 3457
92	Chen H, Lundberg S M, Lee S I. Explaining a series of models by propagating Shapley values [J]. Nat. Commun., 2022, 13: 4512 doi: 10.1038/s41467-022-31384-3 pmid: 35922410
93	Fumagalli F, Muschalik M, Kolpaczki P, et al. SHAP-IQ: Unified approximation of any-order Shapley interactions [A]. Proceedings of the 37th International Conference on Neural Information Processing Systems [C]. New Orleans: Curran Associates Inc., 2024: 11515
94	Lin K, Gao Y Z. Model interpretability of financial fraud detection by group SHAP [J]. Expert Syst. Appl., 2022, 210: 118354
95	Montavon G, Binder A, Lapuschkin S, et al. Layer-wise relevance propagation: An overview [A]. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning [M]. Cham: Springer, 2019: 193
96	Cho H, Lee E K, Choi I S. Layer-wise relevance propagation of interactionnet explains protein-ligand interactions at the atom level [J]. Sci. Rep., 2020, 10: 21155 doi: 10.1038/s41598-020-78169-6 pmid: 33273642
97	Jiang P T, Zhang C B, Hou Q B, et al. LayerCAM: Exploring hierarchical class activation maps for localization [J]. IEEE Trans. Image Process., 2021, 30: 5875
98	Luo Q X, Holm E A, Wang C. A transfer learning approach for improved classification of carbon nanomaterials from tem images [J]. Nanoscale Adv., 2021, 3: 206 doi: 10.1039/d0na00634c pmid: 36131867
99	Ivanovs M, Kadikis R, Ozols K. Perturbation-based methods for explaining deep neural networks: A survey [J]. Pattern Recognit. Lett., 2021, 150: 228
100	Zhou Z M, Cai H, Rong S, et al. Activation maximization generative adversarial nets [J]. arXiv:1703. 02000, 2017
101	Wang G J, Li K Q, Peng L Y, et al. High-throughput automatic integrated material calculations and data management intelligent platform and the application in novel alloys [J]. Acta Metall. Sin., 2021, 58: 75
	王冠杰, 李开旗, 彭力宇等. 高通量自动流程集成计算与数据管理智能平台及其在合金设计中的应用 [J]. 金属学报, 2022, 58: 75 doi: 10.11900/0412.1961.2021.00041
102	Wang G J, Peng L Y, Li K Q, et al. ALKEMIE: An intelligent computational platform for accelerating materials discovery and design [J]. Comput. Mater. Sci., 2021, 186: 110064
103	Wang E P, Wang G J, Zhou J, et al. MBenes-supported single atom catalysts for oxygen reduction and oxygen evolution reaction by first-principles study and machine learning [J]. Natl. Sci. Open, 2024, 3: 20230043
104	Wang G J, Zhou J, Elliott S R, et al. Role of carbon-rings in polycrystalline GeSb₂Te₄ phase-change material [J]. J. Alloys Compd., 2019, 782: 852
105	Gan Y, Wang G J, Zhou J, et al. Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning [J]. npj Comput. Mater., 2021, 7: 176
106	Wang G J, Sun Y Q, Zhou J, et al. PotentialMind: Graph convolutional machine learning potential for Sb-Te binary compounds of multiple stoichiometries [J]. J. Phys. Chem., 2023, 127C: 24724
107	Wang G J, Wang C R, Zhang X G, et al. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations [J]. iScience, 2024, 27: 109673
108	Wang G J, Sun Z M. Atomic insights into device-scale phase-change memory materials using machine learning potential [J]. Sci. Bull., 2023, 68: 3105
109	Sun Y Q, Wang G J, Li K Q, et al. Accelerating the discovery of transition metal borides by machine learning on small data sets [J]. ACS Appl. Mater. Interfaces, 2023, 15: 29278

[1]	LIAO Mingqing, WANG William Yi, WANG Yi, SHANG Shun-Li, LIU Zi-Kui. Zentropy Theory: Bridging Materials Gene to Materials Properties[J]. 金属学报, 2024, 60(10): 1379-1387.
[2]	WANG Guanjie, LI Kaiqi, PENG Liyu, ZHANG Yiming, ZHOU Jian, SUN Zhimei. High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys[J]. 金属学报, 2022, 58(1): 75-88.
[3]	SU Yanjing, FU Huadong, BAI Yang, JIANG Xue, XIE Jianxin. Progress in Materials Genome Engineering in China[J]. 金属学报, 2020, 56(10): 1313-1323.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed