REFERENCES
2. Ghiringhelli LM, Baldauf C, Bereau T, et al. Shared metadata for data-centric materials science. Sci Data 2023;10:626.
3. Tolle KM, Tansley DSW, Hey AJG. The fourth paradigm: data-intensive scientific discovery [point of view]. Proc IEEE 2011;99:1334-7.
4. Agrawal A, Choudhary A. Perspective: materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater 2016;4:053208.
5. Rajan K. Materials informatics: the materials “gene” and big data. Annu Rev Mater Sci 2015;45:153-69.
6. Liu Y, Zhao T, Ju W, Shi S. Materials discovery and design using machine learning. J Materiomics 2017;3:159-77.
8. Rupp M. Machine learning for quantum mechanics in a nutshell. Int J Quantum Chem 2015;115:1058-73.
9. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: recent applications and prospects. NPJ Comput Mater 2017;3:54.
10. Himanen L, Geurts A, Foster AS, Rinke P. Data-driven materials science: status, challenges, and perspectives. Adv Sci 2019;6:1900808.
11. Lin Y, Wang H, Li J, Gao H. Data source selection for information integration in big data era. Inf Sci 2019;479:197.
12. Needs RJ, Pickard CJ. Perspective: role of structure prediction in materials discovery and design. APL Mater 2016;4:053210.
13. Jain A, Shin Y, Persson KA. Computational predictions of energy materials using density functional theory. Nat Rev Mater 2016;1:15004.
14. Oganov AR, Pickard CJ, Zhu Q, Needs RJ. Structure prediction drives materials discovery. Nat Rev Mater 2019;4:331-48.
16. Oganov AR, Lyakhov AO, Valle M. How evolutionary crystal structure prediction works - and why. Acc Chem Res 2011;44:227-37.
17. Oganov AR, Glass CW. Crystal structure prediction using ab initio evolutionary techniques: principles and applications. J Chem Phys 2006;124:244704.
18. Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 2013;65:1501-9.
19. Rosen AS, Fung V, Huck P, et al. High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration. NPJ Comput Mater 2022;8:112.
21. Kresse G, Hafner J. Ab initio molecular-dynamics simulation of the liquid-metal - amorphous-semiconductor transition in germanium. Phys Rev B 1994;49:14251-69.
22. Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 1996;54:11169-86.
23. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem 2018;2:0121.
24. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature 2018;559:547-55.
25. Gubernatis JE, Lookman T. Machine learning in materials design and discovery: examples from the present and suggestions for the future. Phys Rev Mater 2018;2:120301.
26. Goldsmith BR, Esterhuizen J, Liu JX, Bartel CJ, Sutton C. Machine learning for heterogeneous catalyst design and discovery. AIChE J 2018;64:2311-23.
27. Woodley SM, Catlow R. Crystal structure prediction from first principles. Nat Mater 2008;7:937-46.
28. Gražulis S, Chateigner D, Downs RT, et al. Crystallography open database - an open-access collection of crystal structures. J Appl Cryst 2009;42:726-9.
29. Curtarolo S, Setyawan W, Hart GLW, et al. AFLOW: An automatic framework for high-throughput materials discovery. Comput Mater Sci 2012;58:218-26.
30. Gusev VV, Adamson D, Deligkas A, et al. Optimality guarantees for crystal structure prediction. Nature 2023;619:68-72.
32. Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett 2007;98:146401.
33. Lorenz S, Groß A, Scheffler M. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks. Chem Phys Lett 2004;395:210-5.
34. Wu X, Kang F, Duan W, Li J. Density functional theory calculations: a powerful tool to simulate and design high-performance energy storage and conversion materials. Prog Nat Sci 2019;29:247-55.
35. Monticelli L, Tieleman DP. Force fields for classical molecular dynamics. In: Monticelli L, Salonen E. editors. Biomolecular simulations. Methods in molecular biology. Humana Press; 2013. pp. 197-213.
36. Röcken S, Zavadlav J. Accurate machine learning force fields via experimental and simulation data fusion. NPJ Comput Mater 2024;10:69.
37. Pietrucci F. Strategies for the exploration of free energy landscapes: unity in diversity and challenges ahead. Rev Phys 2017;2:32-45.
38. Wales DJ, Bogdan TV. Potential energy and free energy landscapes. J Phys Chem B 2006;110:20765-76.
39. Bonyadi MR, Michalewicz Z. Particle swarm optimization for single objective continuous space problems: a review. Evol Comput 2017;25:1-54.
40. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN'95 - International Conference on Neural Networks; 1995 Nov 27 - Dec 01; Perth, Australia. IEEE; 1995. pp. 1942-8.
41. Gerges F, Zouein G, Azar D. Genetic algorithms with local optima handling to solve sudoku puzzles. In: Proceedings of the 2018 International Conference on Computing and Artificial Intelligence. Association for Computing Machinery; 2018. pp. 19-22.
42. Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 2021;80:8091-126.
43. Mockus J. The Bayesian approach to global optimization. In: Drenick RF, Kozin F, editors. System modeling and optimization. 1982. p. 473-81.
44. Močkus J. On Bayesian methods for seeking the extremum. In: Optimization Techniques IFIP Technical Conference Novosibirsk; 1974 Jul 1-7. 1975. pp. 400-4.
45. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of state calculations by fast computing machines. J Chem Phys 1953;21:1087-92.
46. Khachaturyan A, Semenovsovskaya S, Vainshtein B. The thermodynamic approach to the structure analysis of crystals. Acta Cryst 1981;37:742-54.
47. Corso G, Stark H, Jegelka S, Jaakkola T, Barzilay R. Graph neural networks. Nat Rev Methods Primers 2024;4:17.
48. Zhou J, Cui G, Hu S, et al. Graph neural networks: a review of methods and applications. AI Open 2020;1:57-81.
49. Botu V, Batra R, Chapman J, Ramprasad R. Machine learning force fields: construction, validation, and outlook. J Phys Chem C 2017;121:511-22.
50. Unke OT, Chmiela S, Sauceda HE, et al. Machine learning force fields. Chem Rev 2021;121:10142-86.
51. Kingma DP, Welling M. Auto-encoding variational bayes. arXiv. [Preprint.] Dec 10, 2022 [accessed on 2024 Sep 23]. Available from: https://arxiv.org/abs/1312.6114.
52. Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. arXiv. [Preprint.] Jun 10, 2014 [accessed on 2024 Sep 23]. Available from: https://arxiv.org/abs/1406.2661.
53. Pickard CJ, Needs RJ. Ab initio random structure searching. J Phys Condens Matter 2011;23:053201.
54. Domingos R, Shaik KM, Militzer B. Prediction of novel high-pressure H2O-NaCl and carbon oxide compounds with a symmetry-driven structure search algorithm. Phys Rev B 2018;98:174107.
55. Lu Z, Zhu B, Shires BWB, Scanlon DO, Pickard CJ. Ab initio random structure searching for battery cathode materials. J Chem Phys 2021;154:174111.
56. Morris AJ, Pickard CJ, Needs RJ. Hydrogen/nitrogen/oxygen defect complexes in silicon from computational searches. Phys Rev B 2009;80:144112.
60. Wei SH, Ferreira LG, Bernard JE, Zunger A. Electronic properties of random alloys: special quasirandom structures. Phys Rev B 1990;42:9622-49.
61. Zunger A, Wei SH, Ferreira LG, Bernard JE. Special quasirandom structures. Phys Rev Lett 1990;65:353-6.
62. Zhang X, Wang H, Hickel T, Rogal J, Li Y, Neugebauer J. Mechanism of collective interstitial ordering in Fe–C alloys. Nat Mater 2020;19:849-54.
63. Qin LX, Liang HP, Jiang RL. Structural transition from ordered to disordered of BeZnO2 alloy. Chinese Phys Lett 2020;37:057101.
64. Yang J, Zhang P, Wei SH. Band structure engineering of Cs2AgBiBr6 perovskite through order–disordered transition: a first-principle study. J Phys Chem Lett 2018;9:31-5.
65. Falls Z, Avery P, Wang X, Hilleke KP, Zurek E. The XtalOpt evolutionary algorithm for crystal structure prediction. J Phys Chem C 2021;125:1601-20.
66. Glass CW, Oganov AR, Hansen N. USPEX - evolutionary crystal structure prediction. Comput Phys Commun 2006;175:713-20.
67. Cheng G, Gong XG, Yin WJ. Crystal structure prediction by combining graph network and optimization algorithm. Nat Commun 2022;13:1492.
68. Florence AJ, Johnston A, Price SL, Nowell H, Kennedy AR, Shankland N. An automated parallel crystallisation search for predicted crystal structures and packing motifs of carbamazepine. J Pharm Sci 2006;95:1918-30.
69. Wang Y, Lv J, Zhu L, Ma Y. Crystal structure prediction via particle-swarm optimization. Phys Rev B 2010;82:094116.
70. Wang Y, Lv J, Zhu L, Ma Y. CALYPSO: a method for crystal structure prediction. Comput Phys Commun 2012;183:2063-70.
71. Yang G, Shi S, Yang J, Ma Y. Insight into the role of Li2S2 in Li–S batteries: a first-principles study. J Mater Chem A 2015;3:8865-9.
72. Li D, Tian F, Lv Y, et al. Stability of sulfur nitrides: a first-principles study. J Phys Chem C 2017;121:1515-20.
73. Feng X, Lu S, Pickard CJ, Liu H, Redfern SAT, Ma Y. Carbon network evolution from dimers to sheets in superconducting ytrrium dicarbide under pressure. Commun Chem 2018;1:85.
74. Lv J, Xu M, Lin S, et al. Direct-gap semiconducting tri-layer silicene with 29% photovoltaic efficiency. Nano Energy 2018;51:489-95.
75. Zhang C, Kuang X, Jin Y, et al. Prediction of stable ruthenium silicides from first-principles calculations: stoichiometries, crystal structures, and physical properties. ACS Appl Mater Interfaces 2015;7:26776-82.
76. Deaven DM, Ho KM. Molecular geometry optimization with a genetic algorithm. Phys Rev Lett 1995;75:288-91.
77. Lyakhov AO, Oganov AR, Stokes HT, Zhu Q. New developments in evolutionary structure prediction algorithm USPEX. Comput Phys Commun 2013;184:1172-82.
78. Liu W, Liang H, Duan Y, Wu Z. Predicting copper gallium diselenide and band structure engineering through order-disordered transition. Phys Rev Mater 2019;3:125405.
79. Lv F, Liang H, Duan Y. Funnel-shaped electronic structure and enhanced thermoelectric performance in ultralight Cx(BN)1−x biphenylene networks. Phys Rev B 2023;107:045422.
80. Liang H, Zhong H, Huang S, Duan Y. 3-X structural model and common characteristics of anomalous thermal transport: the case of two-dimensional boron carbides. J Phys Chem Lett 2021;12:10975-80.
81. Liang H, Duan Y. Structural reconstruction and visible-light absorption versus internal electrostatic field in two-dimensional GaN–ZnO alloys. Nanoscale 2021;13:11994-2003.
82. Wang J, Hanzawa K, Hiramatsu H, et al. Exploration of stable strontium phosphide-based electrides: theoretical structure prediction and experimental validation. J Am Chem Soc 2017;139:15668-80.
83. Yu S, Zeng Q, Oganov AR, Frapper G, Zhang L. Phase stability, chemical bonding and mechanical properties of titanium nitrides: a first-principles study. Phys Chem Chem Phys 2015;17:11763-9.
84. Duan D, Liu Y, Tian F, et al. Pressure-induced metallization of dense (H2S)2H2 with high-Tc superconductivity. Sci Rep 2014;4:6968.
86. Wu SQ, Ji M, Wang CZ, et al. An adaptive genetic algorithm for crystal structure prediction. J Phys Condens Matter 2013;26:035402.
87. Podryabinkin EV, Tikhonov EV, Shapeev AV, Oganov AR. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Phys Rev B 2019;99:064114.
88. Shahriari B, Swersky K, Wang Z, Adams RP, de Freitas N. Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 2016;104:148-75.
89. Kaappa S, del Río EG, Jacobsen KW. Global optimization of atomic structures with gradient-enhanced Gaussian process regression. Phys Rev B 2021;103:174114.
90. Kaappa S, Larsen C, Jacobsen KW. Atomic structure optimization with machine-learning enabled interpolation between chemical elements. Phys Rev Lett 2021;127:166001.
91. Bisbo MK, Hammer B. Global optimization of atomic structure enhanced by machine learning. Phys Rev B 2022;105:245404.
92. Regis RG. Trust regions in Kriging-based optimization with expected improvement. Eng Optim 2016;48:1037-59.
93. Titsias M. Variational learning of Inducing variables in sparse Gaussian processes. In: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. 2009. pp. 567-74. Available from: https://proceedings.mlr.press/v5/titsias09a.html. [Last accessed on 23 Sep 2024].
94. Siemenn AE, Ren Z, Li Q, Buonassisi T. Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI). NPJ Comput Mater 2023;9:79.
95. Kirkpatrick S, Gelatt CD Jr, Vecchi MP. Optimization by simulated annealing. Science 1983;220:671-80.
97. Doll K, Schön JC, Jansen M. Global exploration of the energy landscape of solids on the ab initio level. Phys Chem Chem Phys 2007;9:6128-33.
98. Doll K, Jansen M. Ab initio energy landscape of GeF2: a system featuring lone pair structure candidates. Angew Chem Int Ed 2011;50:4627-32.
99. Doll K, Schön JC, Jansen M. Structure prediction based on ab initio simulated annealing for boron nitride. Phys Rev B 2008;78:144110.
100. Timmermann J, Lee Y, Staacke CG, Margraf JT, Scheurer C, Reuter K. Data-efficient iterative training of Gaussian approximation potentials: Application to surface structure determination of rutile IrO2 and RuO2. J Chem Phys 2021;155:244107.
101. Fischer CC, Tibbetts KJ, Morgan D, Ceder G. Predicting crystal structure by merging data mining with quantum mechanics. Nat Mater 2006;5:641-6.
103. Hautier G, Fischer C, Ehrlacher V, Jain A, Ceder G. Data mined ionic substitutions for the discovery of new compounds. Inorg Chem 2011;50:656-63.
104. Sun W, Bartel CJ, Arca E, et al. A map of the inorganic ternary metal nitrides. Nat Mater 2019;18:732-9.
105. Deng B, Zhong P, Jun K, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat Mach Intell 2023;5:1031-41.
106. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature 2023;624:80-5.
107. Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 2018;120:145301.
108. Isayev O, Oses C, Toher C, Gossett E, Curtarolo S, Tropsha A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat Commun 2017;8:15679.
109. Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian process regression for materials and molecules. Chem Rev 2021;121:10073-141.
110. Zhang L, Han J, Wang H, Saidi WA, Car R, E W. End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems; Montréal, Canada. 2018. Available from: https://proceedings.neurips.cc/paper_files/paper/2018/file/e2ad76f2326fbc6b56a45a56c59fafdb-Paper.pdf. [Last accessed on 23 Sep 2024].
111. Noh J, Gu GH, Kim S, Jung Y. Machine-enabled inverse design of inorganic solid materials: promises and challenges. Chem Sci 2020;11:4871-81.
112. Damewood J, Karaguesian J, Lunger JR, et al. Representations of materials for machine learning. Annu Rev Mater Sci 2023;53:399-426.
113. Greeley J, Jaramillo TF, Bonde J, Chorkendorff I, Nørskov JK. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat Mater 2006;5:909-13.
114. Yeo BC, Nam H, Nam H, et al. High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts. NPJ Comput Mater 2021;7:137.
115. Rittiruam M, Noppakhun J, Setasuban S, et al. High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys. Sci Rep 2022;12:16653.
116. Szymanski NJ, Rendy B, Fei Y, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023;624:86-91.
117. Chmiela S, Vassilev-Galindo V, Unke OT, et al. Accurate global machine learning force fields for molecules with hundreds of atoms. Sci Adv 2023;9:eadf0873.
118. Sauceda HE, Gálvez-González LE, Chmiela S, Paz-Borbón LO, Müller KR, Tkatchenko A. BIGDML - towards accurate quantum machine learning force fields for materials. Nat Commun 2022;13:3733.
119. Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 2013;1:011002.
120. Choudhary K, Garrity KF, Reid ACE, et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. NPJ Comput Mater 2020;6:173.
121. Zeni C, Pinsler R, Zügner D, et al. MatterGen: a generative model for inorganic materials design. arXiv. [Preprint.] Jan 29, 2024 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2312.03687.
122. Xie T, Fu X, Ganea OE, Barzilay R, Jaakkola T. Crystal diffusion variational autoencoder for periodic material generation. arXiv. [Preprint.] Mar 14, 2022 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2110.06197.
123. Behler J. Constructing high-dimensional neural network potentials: a tutorial review. Int J Quantum Chem 2015;115:1032-50.
124. Hoffmann J, Maestrati L, Sawada Y, Tang J, Sellier JM, Bengio Y. Data-driven approach to encoding and decoding 3-D crystal structures. arXiv. [Preprint.] Sep 3, 2019 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1909.00949.
125. Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet - a deep learning architecture for molecules and materials. J Chem Phys 2018;148:241722.
126. Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater 2019;31:3564-72.
127. Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A. Quantum-chemical insights from deep tensor neural networks. Nat Commun 2017;8:13890.
128. Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. arXiv. [Preprint.] Jun 12, 2017 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1704.01212.
129. Sanderson RT. An interpretation of bond lengths and a classification of bonds. Science 1951;114:670-2.
130. Sanderson RT. An explanation of chemical variations within periodic major groups. J Am Chem Soc 1952;74:4792-4.
131. Cordero B, Gómez V, Platero-Prats AE, et al. Covalent radii revisited. Dalton Trans 2008:2832-8.
132. Haynes WM. CRC handbook of chemistry and physics. CRC Press; 2014. Available from: https://doi.org/10.1201/b17118. [Last accessed on Sep 23 2024].
133. Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. NPJ Comput Mater 2021;7:185.
134. Behler J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J Chem Phys 2011;134:074106.
135. Bartók AP, De S, Poelking C, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv 2017;3:e1701816.
136. Irwin JJ, Tang KG, Young J, et al. ZINC20 - a free ultralarge-scale chemical database for ligand discovery. J Chem Inf Model 2020;60:6065-73.
137. Haastrup S, Strange M, Pandey M, et al. The computational 2D materials database: high-throughput modeling and discovery of atomically thin crystals. 2D Mater 2018;5:042002.
138. Zhou J, Shen L, Costa MD, et al. 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches. Sci Data 2019;6:86.
139. Alizamir M, Kisi O, Ahmed AN, et al. Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One 2020;15:e0231055.
140. Salehin I, Islam MS, Saha P, et al. AutoML: a systematic review on automated machine learning with neural architecture search. J Inf Intell 2024;2:52-81.
141. Ali Y, Hussain F, Haque MM. Advances, challenges, and future research needs in machine learning-based crash prediction models: a systematic review. Accid Anal Prev 2024;194:107378.
142. Jun K, Sun Y, Xiao Y, et al. Lithium superionic conductors with corner-sharing frameworks. Nat Mater 2022;21:924-31.
143. Zhong M, Tran K, Min Y, et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 2020;581:178-83.
144. Leitherer A, Ziletti A, Ghiringhelli LM. Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning. Nat Commun 2021;12:6234.
145. Duvenaud DK, Maclaurin D, Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints. arXiv. [Preprint.] Nov 3, 2015 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1509.09292.
146. Li Y, Tarlow D, Brockschmidt M, Zemel R. Gated graph sequence neural networks. arXiv. [Preprint.] Sep 22, 2017 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1511.05493.
147. Battaglia PW, Pascanu R, Lai M, Rezende D, Kavukcuoglu K. Interaction networks for learning about objects, relations and physics. arXiv. [Preprint.] Dec 1, 2016 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1612.00222.
148. Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 2016;30:595-608.
149. Bruna J, Zaremba W, Szlam A, LeCun Y. Spectral networks and locally connected networks on graphs. arXiv. [Preprint.] May 21, 2014 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1312.6203.
150. Li CN, Liang HP, Zhang X, Lin Z, Wei SH. Graph deep learning accelerated efficient crystal structure search and feature extraction. NPJ Comput Mater 2023;9:176.
151. Li C, Liang H, Duan Y, Lin Z. Machine-learning accelerated annealing with fitting-search style for multicomponent alloy structure predictions. Phys Rev Mater 2023;7:033802.
152. Liang HP, Geng S, Jia T, et al. Unveiling disparities and promises of Cu and Ag chalcopyrites for thermoelectrics. Phys Rev B 2024;109:035205.
153. Liang HP, Li CN, Zhou R, et al. Critical role of configurational disorder in stabilizing chemically unfavorable coordination in complex compounds. J Am Chem Soc 2024;146:16222-8.
154. Harrison JA, Schall JD, Maskey S, Mikulski PT, Knippenberg MT, Morrow BH. Review of force fields and intermolecular potentials used in atomistic computational materials research. Appl Phys Rev 2018;5:031104.
155. Senftle TP, Hong S, Islam MM, et al. The ReaxFF reactive force-field: development, applications and future directions. NPJ Comput Mater 2016;2:15011.
156. Batzner S, Musaelian A, Sun L, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun 2022;13:2453.
157. Batatia I, Kovács DP, Simm GNC, Ortner C, Csányi G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. arXiv. [Preprint.] Jan 26, 2023 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2206.07697.
158. Gale JD, LeBlanc LM, Spackman PR, Silvestri A, Raiteri P. A universal force field for materials, periodic GFN-FF: implementation and examination. J Chem Theory Comput 2021;17:7827-49.
159. Cole DJ, Horton JT, Nelson L, Kurdekar V. The future of force fields in computer-aided drug design. Future Med Chem 2019;11:2359-63.
160. Robustelli P, Piana S, Shaw DE. Developing a molecular dynamics force field for both folded and disordered protein states. Proc Natl Acad Sci 2018;115:E4758-66.
161. Deringer VL, Caro MA, Csányi G. Machine learning interatomic potentials as emerging tools for materials science. Adv Mater 2019;31:1902765.
162. Gao H, Wang J, Sun J. Improve the performance of machine-learning potentials by optimizing descriptors. J Chem Phys 2019;150:244110.
163. Liu P, Verdi C, Karsai F, Kresse G. Phase transitions of zirconia: machine-learned force fields beyond density functional theory. Phys Rev B 2022;105:L060102.
164. Zhang L, Han J, Wang H, Car R, EW. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys Rev Lett 2018;120:143001.
165. Wang J, Gao H, Han Y, et al. MAGUS: machine learning and graph theory assisted universal structure searcher. Natl Sci Rev 2023;10:nwad128.
166. Xia K, Gao H, Liu C, et al. A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search. Sci Bull 2018;63:817-24.
167. Liu C, Gao H, Wang Y, et al. Multiple superionic states in helium-water compounds. Nat Phys 2019;15:1065-70.
168. Hong C, Choi JM, Jeong W, et al. Training machine-learning potentials for crystal structure prediction using disordered structures. Phys Rev B 2020;102:224104.
169. Tong Q, Xue L, Lv J, Wang Y, Ma Y. Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface. Faraday Discuss 2018;211:31-43.
170. Tong Q, Gao P, Liu H, et al. Combining machine learning potential and structure prediction for accelerated materials design and discovery. J Phys Chem Lett 2020;11:8710-20.
171. Kang S, Jeong W, Hong C, Hwang S, Yoon Y, Han S. Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials. npj Comput Mater 2022;8:108.
172. Hwang S, Jung J, Hong C, Jeong W, Kang S, Han S. Stability and equilibrium structures of unknown ternary metal oxides explored by machine-learned potentials. J Am Chem Soc 2023;145:19378-86.
173. Deringer VL, Pickard CJ, Csányi G. Data-driven learning of total and local energies in elemental boron. Phys Rev Lett 2018;120:156001.
174. Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem Sci 2017;8:3192-203.
175. Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proc IEEE 2021;109:43-76.
176. Zhang Q, Zhu S. Visual interpretability for deep learning: a survey. Frontiers Inf Technol Electronic Eng 2018;19:27-39.
177. Han S, Pool J, Tran J, Dally WJ. Learning both weights and connections for efficient neural networks. arXiv. [Preprint.] Oct 30, 2015 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1506.02626.
178. Cheng Y, Wang D, Zhou P, Zhang T. Model compression and acceleration for deep neural networks: the principles, progress, and challenges. IEEE Signal Process Mag 2018;35:126-36.
179. Ramesh A, Pavlov M, Goh G, et al. Zero-shot text-to-image generation. arXiv. [Preprint.] Feb 26, 2021 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2102.12092.
180. Yu J, Xu Y, Koh JY, et al. Scaling autoregressive models for content-rich rext-to-image generation. arXiv. [Preprint.] Jun 22, 2022 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2206.10789.
181. Ho J, Chan W, Saharia C, et al. Imagen video: high definition video generation with diffusion models. arXiv. [Preprint.] Oct 5, 2022 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2210.02303.
182. Singer U, Polyak A, Hayes T, et al. Make-a-video: text-to-video generation without text-video data. arXiv. [Preprint.] Sep 29, 2022 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2209.14792.
183. Anil R, Dai AM, Firat O, et al. PaLM 2 technical report. arXiv. [Preprint.] Sep 13, 2023 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2305.10403.
184. Noh J, Kim J, Stein HS, et al. Inverse design of solid-state materials via a continuous representation. Matter 2019;1:1370-84.
185. Ren Z, Tian SIP, Noh J, et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter 2022;5:314-35.
186. Kim S, Noh J, Gu GH, Aspuru-Guzik A, Jung Y. Generative adversarial networks for crystal structure prediction. ACS Cent Sci 2020;6:1412-20.
187. Nouira A, Sokolovska N, Crivello JC. CrystalGAN: learning to discover crystallographic structures with generative adversarial networks. arXiv. [Preprint.] May 25, 2019 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.1810.11203.
188. Kim B, Lee S, Kim J. Inverse design of porous materials using artificial neural networks. Sci Adv 2020;6:eaax9324.
189. Fung V, Zhang J, Hu G, Ganesh P, Sumpter BG. Inverse design of two-dimensional materials with invertible neural networks. NPJ Comput Mater 2021;7:200.
190. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. arXiv. [Preprint.] Dec 16, 2020 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2006.11239.
191. Zhang W, Oganov AR, Goncharov AF, et al. Unexpected stable stoichiometries of sodium chlorides. Science 2013;342:1502-5.
192. Zhao C, Duan Y, Gao J, et al. Unexpected stable phases of tungsten borides. Phys Chem Chem Phys 2018;20:24665-70.
193. Lonie DC, Zurek E. XtalOpt: an open-source evolutionary algorithm for crystal structure prediction. Comput Phys Commun 2011;182:372-87.
194. Baettig P, Zurek E. Pressure-stabilized sodium polyhydrides: NaHn (n > 1). Phys Rev Lett 2011;106:237002.
197. Pickard CJ, Needs RJ. Highly compressed ammonia forms an ionic crystal. Nat Mater 2008;7:775-9.
198. Lv J, Wang Y, Zhu L, Ma Y. Predicted novel high-pressure phases of lithium. Phys Rev Lett 2011;106:015503.
199. Liu H, Naumov II, Hoffmann R, Ashcroft NW, Hemley RJ. Potential high-TC superconducting lanthanum and yttrium hydrides at high pressure. Proc Natl Acad Sci USA 2017;114:6990.
200. Wang H, Song Y, Huang G, et al. Seeded growth of single-crystal black phosphorus nanoribbons. Nat Mater 2024;23:470-8.
201. Tipton WW, Hennig RG. A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials. J Phys Condens Matter 2013;25:495401.
202. Feng J, Hennig RG, Ashcroft NW, Hoffmann R. Emergent reduction of electronic state dimensionality in dense ordered Li-Be alloys. Nature 2008;451:445-8.
203. Tipton WW, Bealing CR, Mathew K, Hennig RG. Structures, phase stabilities, and electrical potentials of Li-Si battery anode materials. Phys Rev B 2013;87:184114.
204. Zhao X, Nguyen MC, Zhang WY, et al. Exploring the structural complexity of intermetallic compounds by an adaptive genetic algorithm. Phys Rev Lett 2014;112:045502.
205. Umemoto K, Wentzcovitch RM, Wu S, Ji M, Wang CZ, Ho KM. Phase transitions in MgSiO3 post-perovskite in super-Earth mantles. Earth Planet Sci Lett 2017;478:40-5.
206. Liu ZL. Muse: multi-algorithm collaborative crystal structure prediction. Comput Phys Commun 2014;185:1893-900.
207. Li X, Wang H, Lv J, Liu Z. Phase diagram and physical properties of iridium tetraboride from first principles. Phys Chem Chem Phys 2016;18:12569-75.
208. Liu ZL, Jia H, Li R, Zhang XL, Cai LC. Unexpected coordination number and phase diagram of niobium diselenide under compression. Phys Chem Chem Phys 2017;19:13219-29.
209. Zhang YY, Gao W, Chen S, Xiang H, Gong XG. Inverse design of materials by multi-objective differential evolution. Comput Mater Sci 2015;98:51.
210. Chen HZ, Zhang YY, Gong X, Xiang H. Predicting new TiO2 phases with low band gaps by a multiobjective global optimization approach. J Phys Chem C 2014;118:2333-7.
211. Yang JH, Zhang Y, Yin WJ, Gong XG, Yakobson BI, Wei SH. Two-dimensional SiS layers with promising electronic and optoelectronic properties: theoretical prediction. Nano Lett 2016;16:1110-7.
212. Olson MA, Bhatia S, Larson P, Militzer B. Prediction of chlorine and fluorine crystal structures at high pressure using symmetry driven structure search with geometric constraints. J Chem Phys 2020;153:094111.
213. Hajinazar S, Thorn A, Sandoval ED, Kharabadze S, Kolmogorov AN. MAISE: Construction of neural network interatomic models and evolutionary structure optimization. Comput Phys Commun 2021;259:107679.
214. Kolmogorov AN, Shah S, Margine ER, Bialon AF, Hammerschmidt T, Drautz R. New superconducting and semiconducting Fe-B compounds predicted with an ab initio evolutionary search. Phys Rev Lett 2010;105:217003.
215. Shao J, Beaufils C, Kolmogorov AN. Ab initio engineering of materials with stacked hexagonal tin frameworks. Sci Rep 2016;6:28369.
216. Bisbo MK, Hammer B. Efficient global structure optimization with a machine-learned surrogate model. Phys Rev Lett 2020;124:086102.
217. Yamashita T, Kanehira S, Sato N, et al. CrySPY: a crystal structure prediction tool accelerated by machine learning. Science Technol Adv Mater 2021;1:87-97.
218. Terayama K, Yamashita T, Oguchi T, Tsuda K. Fine-grained optimization method for crystal structure prediction. npj Comput Mater 2018;4:32.
219. Gao H, Wang J, Guo Z, Sun J. Determining dimensionalities and multiplicities of crystal nets. NPJ Comput Mater 2020;6:143.
220. Yang S, Cho K, Merchant A, et al. Scalable diffusion for materials generation. arXiv. [Preprint.] Jun 3, 2024 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2311.09235.
221. Jiao R, Huang W, Lin P, et al. Crystal structure prediction by joint equivariant diffusion. arXiv. [Preprint.] Mar 7, 2024 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2309.04475.
222. Gruver N, Sriram A, Madotto A, Wilson AG, Zitnick CL, Ulissi Z. Fine-tuned language models generate stable inorganic materials as text. arXiv. [Preprint.] Feb 6, 2024 [accessed on 2024 Sep 23]. Available from: https://doi.org/10.48550/arXiv.2402.04379.
223. Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data 2019;6:60.
224. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929-58. Available from: https://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf?utm_content=buffer79b43&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer. [Last accessed on 23 Sep 2024].
225. Dietterich TG. Ensemble methods in machine learning. In: Multiple classifier systems. Springer Berlin Heidelberg; 2000. pp. 1-15.
226. Peherstorfer B, Willcox K, Gunzburger M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. SIAM Review 2018;60:550.