REFERENCES
1. Hilgenkamp H, Mannhart J. Grain boundaries in high-Tc superconductors. Rev Mod Phys 2002;74:485-549.
2. Dillon SJ, Tang M, Carter WC, Harmer MP. Complexion: a new concept for kinetic engineering in materials science. Acta Mater 2007;55:6208-18.
3. Robertson J. High dielectric constant gate oxides for metal oxide Si transistors. Rep Prog Phys 2006;69:327-96.
4. Li YF, Liu ZP. Smallest stable Si/SiO2 interface that suppresses quantum tunneling from machine-learning-based global search. Phys Rev Lett 2022;128:226102.
5. Franklin AD. Nanomaterials in transistors: from high-performance to thin-film applications. Science 2015;349:aab2750.
6. Buczko R, Pennycook SJ, Pantelides ST. Bonding arrangements at the Si-SiO2 and SiC-SiO2 interfaces and a possible origin of their contrasting properties. Phys Rev Lett 2000;84:943-6.
7. Tu Y, Tersoff J. Structure and energetics of the Si- SiO2 interface. Phys Rev Lett 2000;84:4393-6.
8. Tang H, Deng Z, Lin Z, et al. Probing solid-solid interfacial reactions in all-solid-state sodium-ion batteries with first-principles calculations. Chem Mater 2018;30:163-73.
9. Han X, Gong Y, Fu KK, et al. Negating interfacial impedance in garnet-based solid-state Li metal batteries. Nat Mater 2017;16:572-9.
10. Ohta N, Takada K, Zhang L, Ma R, Osada M, Sasaki T. Enhancement of the high-rate capability of solid-state lithium batteries by nanoscale interfacial modification. Adv Mater 2006;18:2226-9.
11. Takada K, Ohta N, Zhang L, et al. Interfacial modification for high-power solid-state lithium batteries. Solid State Ion 2008;179:1333-7.
12. Li LF, Li YF, Liu ZP. CO2 photoreduction via quantum tunneling: thin TiO2-coated GaP with coherent interface to achieve electron tunneling. ACS Catal 2019;9:5668-78.
13. Chua AL, Benedek NA, Chen L, Finnis MW, Sutton AP. A genetic algorithm for predicting the structures of interfaces in multicomponent systems. Nat Mater 2010;9:418-22.
14. Zhang Z, Sigle W, Phillipp F, Rühle M. Direct atom-resolved imaging of oxides and their grain boundaries. Science 2003;302:846-9.
15. Sun R, Wang Z, Saito M, Shibata N, Ikuhara Y. Atomistic mechanisms of nonstoichiometry-induced twin boundary structural transformation in titanium dioxide. Nat Commun 2015;6:7120.
16. von Alfthan S, Haynes PD, Kaski K, Sutton AP. Are the structures of twist grain boundaries in silicon ordered at 0 K? Phys Rev Lett 2006;96:055505.
17. Zhang J, Wang CZ, Ho KM. Finding the low-energy structures of Si[001] symmetric tilted grain boundaries with a genetic algorithm. Phys Rev B 2009;80:174102.
18. Benedek NA, Chua ALS, Elsässer C, Sutton AP, Finnis MW. Interatomic potentials for strontium titanate: an assessment of their transferability and comparison with density functional theory. Phys Rev B 2008;78:064110.
19. Peacock PW, Xiong K, Tse K, Robertson J. Bonding and interface states of Si:HfO2 and Si:ZrO2 interfaces. Phys Rev B 2006;73:075328.
20. Woicik JC, Shirley EL, Hellberg CS, et al. Ferroelectric distortion in SrTiO3 thin films on Si(001) by x-ray absorption fine structure spectroscopy: Experiment and first-principles calculations. Phys Rev B 2007;75:140103.
21. Barcaro G, Sementa L, Negreiros FR, Thomas IO, Vajda S, Fortunelli A. Atomistic and electronic structure methods for nanostructured oxide interfaces. In: Netzer FP, Fortunelli A, editors. Oxide materials at the two-dimensional limit. Cham: Springer International Publishing; 2016. p. 39-90.
22. Wales DJ, Doye JPK. Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms. J Phys Chem A 1997;101:5111-6.
23. Zhao X, Shu Q, Nguyen MC, et al. Interface structure prediction from first-principles. J Phys Chem C 2014;118:9524-30.
25. Romeijn HE, Smith RL. Simulated annealing for constrained global optimization. J Glob Optim 1994;5:101-26.
26. Wang Y, Lv J, Zhu L, Ma Y. CALYPSO: a method for crystal structure prediction. Comput Phys Commun 2012;183:2063-70.
27. Wang H, Wang Y, Lv J, Li Q, Zhang L, Ma Y. CALYPSO structure prediction method and its wide application. Comput Mater Sci 2016;112:406-15.
28. Li ZL, Li ZM, Cao HY, et al. What are grain boundary structures in graphene? Nanoscale 2014;6:4309-15.
29. Schusteritsch G, Pickard CJ. Predicting interface structures: from SrTiO3 to graphene. Phys Rev B 2014;90:035424.
30. Zhu Q, Samanta A, Li B, Rudd RE, Frolov T. Predicting phase behavior of grain boundaries with evolutionary search and machine learning. Nat Commun 2018;9:467.
31. Shang C, Liu ZP. Stochastic surface walking method for structure prediction and pathway searching. J Chem Theory Comput 2013;9:1838-45.
32. Shang C, Zhang XJ, Liu ZP. Stochastic surface walking method for crystal structure and phase transition pathway prediction. Phys Chem Chem Phys 2014;16:17845-56.
33. Ma S, Liu ZP. Machine learning for atomic simulation and activity prediction in heterogeneous catalysis: current status and future. ACS Catal 2020;10:13213-26.
35. Gao B, Gao P, Lu S, Lv J, Wang Y, Ma Y. Interface structure prediction via CALYPSO method. Sci Bull 2019;64:301-9.
36. Wang Y, Lv J, Zhu L, et al. Materials discovery via CALYPSO methodology. J Phys Condens Matter 2015;27:203203.
37. Shi Y, Song B, Shahbazian-Yassar R, Zhao J, Saidi WA. Experimentally validated structures of supported metal nanoclusters on MoS2. J Phys Chem Lett 2018;9:2972-8.
38. 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.
39. Zhao WN, Zhu SC, Li YF, Liu ZP. Three-phase junction for modulating electron-hole migration in anatase-rutile photocatalysts. Chem Sci 2015;6:3483-94.
40. Zhu ZY, Li YF, Shang C, Liu ZP. Thermodynamics and catalytic activity of ruthenium oxides grown on ruthenium metal from a machine learning atomic simulation. J Phys Chem C 2021;125:17088-96.
41. Hu YF, Li YF, Liu ZP. Structural origin for efficient photoelectrochemical water splitting over Fe-modified BiVO4. ACS Catal 2023;13:10167-76.
42. Hao Y, Kang Y, Wang S, et al. Electrode/electrolyte synergy for concerted promotion of electron and proton transfers toward efficient neutral water oxidation. Angew Chem Int Ed 2023;62:e202303200.
43. Huang SD, Shang C, Zhang XJ, Liu ZP. Material discovery by combining stochastic surface walking global optimization with a neural network. Chem Sci 2017;8:6327-37.
44. Huang SD, Shang C, Kang PL, Zhang XJ, Liu ZP. LASP: Fast global potential energy surface exploration. WIREs Comput Mol Sci 2019;9:e1415.
45. Kang P, Shang C, Liu Z. Recent implementations in LASP 3.0: Global neural network potential with multiple elements and better long-range description. Chin J Chem Phys 2021;34:583-90.
46. Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF. Generative models for molecular discovery: recent advances and challenges. WIREs Comput Mol Sci 2022;12:e1608.
47. Chen L, Zhang W, Nie Z, Li S, Pan F. Generative models for inverse design of inorganic solid materials. J Mater Inf 2021;1:4.
48. 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.
49. 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.
50. Nouira A, Sokolovska N, Crivello J-CJapa. Crystalgan: learning to discover crystallographic structures with generative adversarial networks. arXiv. [Preprint.] May 25, 2019 [accessed 2023 October 14]. Available from: https://arxiv.org/abs/1810.11203.
51. Lyngby P, Thygesen KS. Data-driven discovery of 2D materials by deep generative models. npj Comput Mater 2022;8:232.
52. Gebauer NWA, Gastegger M, Hessmann SSP, Müller KR, Schütt KT. Inverse design of 3d molecular structures with conditional generative neural networks. Nat Commun 2022;13:973.
53. Moreno JJG, Nolan M. Ab initio study of the atomic level structure of the rutile TiO2(110)-titanium nitride (TiN) interface. ACS Appl Mater Interfaces 2017;9:38089-100.
54. Andrews LC, Bernstein HJ. The geometry of niggli reduction I: the boundary polytopes of the niggli cone. arXiv. [Preprint.] Aug 9, 2013 [accessed 2023 October 14]. Available from: https://arxiv.org/abs/1203.5146.
55. Andrews LC, Bernstein HJ. The geometry of niggli reduction II: BGAOL--embedding niggli reduction. arXiv. [Preprint.] May 28, 2013 [accessed 2023 October 14]. Available from: https://arxiv.org/abs/1305.6561.
56. McGill KJ, Asadi M, Karakasheva MT, Andrews LC, Bernstein HJ. The geometry of niggli reduction III: SAUC--search of alternate unit cells. arXiv. [Preprint.] Jul 9, 2013 [accessed 2023 October 14]. Available from: https://arxiv.org/abs/1307.1811.
57. Wang Y, Miao M, Lv J, et al. An effective structure prediction method for layered materials based on 2D particle swarm optimization algorithm. J Chem Phys 2012;137:224108.
58. Zhu L, Liu H, Pickard CJ, Zou G, Ma Y. Reactions of xenon with iron and nickel are predicted in the Earth’s inner core. Nat Chem 2014;6:644-8.
59. Lv J, Wang Y, Zhu L, Ma Y. Particle-swarm structure prediction on clusters. J Chem Phys 2012;137:084104.
60. Lu S, Wang Y, Liu H, Miao MS, Ma Y. Self-assembled ultrathin nanotubes on diamond (100) surface. Nat Commun 2014;5:3666.
61. Gao B, Shao X, Lv J, Wang Y, Ma Y. Structure prediction of atoms adsorbed on two-dimensional layer materials: method and applications. J Phys Chem C 2015;119:20111-8.
62. Bowles JS, Mackenzie JK. The crystallography of martensite transformations I. Acta Metall 1954;2:129-37.
63. Mackenzie JK, Bowles JS. The crystallography of martensite transformations II. Acta Metall 1954;2:138-47.
64. Wayman CM. The phenomenological theory of martensite crystallography: interrelationships. Metall Mater Trans A 1994;25:1787-95.
65. Krivy I, Gruber B. A unified algorithm for determining the reduced (Niggli) cell. Acta Crystallogr A 1976;32:297-8.
66. Gruber B. The relationship between reduced cells in a general Bravais lattice. Acta Crystallogr A 1973;29:433-40.
67. Grosse-Kunstleve RW, Sauter NK, Adams PD. Numerically stable algorithms for the computation of reduced unit cells. Acta Crystallogr A 2004;60:1-6.
68. Andrews LC, Bernstein HJ, Sauter NK. Selling reduction versus Niggli reduction for crystallographic lattices. Acta Crystallogr A Found Adv 2019;75:115-20.
69. Pyykkö P, Atsumi M. Molecular single-bond covalent radii for elements 1-118. Chemistry 2009;15:186-97.
70. 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.
71. Blatov VA. Voronoi-dirichlet polyhedra in crystal chemistry: theory and applications. Crystallogr Rev 2004;10:249-318.
73. Witman M, Ling S, Boyd P, et al. Cutting materials in half: a graph theory approach for generating crystal surfaces and its prediction of 2D zeolites. ACS Cent Sci 2018;4:235-45.
74. Li YF. First-principles simulations for morphology and structural evolutions of catalysts in oxygen evolution reaction. ChemSusChem 2019;12:1846-57.
75. Li JL, Li YF, Liu ZP. In situ structure of a Mo-doped Pt-Ni catalyst during electrochemical oxygen reduction resolved from machine learning-based grand canonical global optimization. JACS Au 2023;3:1162-75.
76. Huang SD, Shang C, Kang PL, Liu ZP. Atomic structure of boron resolved using machine learning and global sampling. Chem Sci 2018;9:8644-55.
77. Zhang XJ, Shang C, Liu ZP. From atoms to fullerene: stochastic surface walking solution for automated structure prediction of complex material. J Chem Theory Comput 2013;9:3252-60.
78. Ma S, Huang SD, Liu ZP. Dynamic coordination of cations and catalytic selectivity on zinc–chromium oxide alloys during syngas conversion. Nat Catal 2019;2:671-7.
79. Shi YF, Kang PL, Shang C, Liu ZP. Methanol synthesis from CO2/CO mixture on Cu-Zn catalysts from microkinetics-guided machine learning pathway search. J Am Chem Soc 2022;144:13401-14.
80. Liu QY, Shang C, Liu ZP. In situ active site for fe-catalyzed fischer-tropsch synthesis: recent progress and future challenges. J Phys Chem Lett 2022;13:3342-52.
81. Lin C, Li JL, Li X, et al. In-situ reconstructed Ru atom array on α-MnO2 with enhanced performance for acidic water oxidation. Nat Catal 2021;4:1012-23.
82. Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett 2007;98:146401.
83. Behler J. Representing potential energy surfaces by high-dimensional neural network potentials. J Phys Condens Matter 2014;26:183001.
84. 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.
85. Park CW, Wolverton C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys Rev Mater 2020;4:063801.
86. Bartók AP, Payne MC, Kondor R, Csányi G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys Rev Lett 2010;104:136403.
87. Lu X, Xie Z, Wu X, Li M, Cai W. Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks. Chem Eng Sci 2022;259:117813.
88. Westermayr J, Gastegger M, Marquetand P. Combining SchNet and SHARC: the SchNarc machine learning approach for excited-state dynamics. J Phys Chem Lett 2020;11:3828-34.
89. Li H, Guo Y, Robertson J, Okuno Y. Ab-initio simulations of higher Miller index Si:SiO2 interfaces for fin field effect transistor and nanowire transistors. J Appl Phys 2016;119:054103.
90. Korkin A, Greer JC, Bersuker G, Karasiev VV, Bartlett RJ. Computational design of Si/SiO2 interfaces: Stress and strain on the atomic scale. Phys Rev B 2006;73:165312.
91. Ogata S, Ohno S, Tanaka M, Mori T, Horikawa T, Yasuda T. SiO2/Si interfaces on high-index surfaces: re-evaluation of trap densities and characterization of bonding structures. Appl Phys Lett 2011;98:092906.
92. Colinge JP, Quinn AJ, Floyd L, et al. Low-temperature electron mobility in Trigate SOI MOSFETs. IEEE Electron Device Lett 2006;27:120-2.
93. Li YF. First-principles prediction of the ZnO morphology in the perovskite solar cell. J Phys Chem C 2019;123:14164-72.
94. Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B Condens Matter 1996;54:11169-86.
95. Hutter J, Iannuzzi M, Schiffmann F, VandeVondele J. cp2k: atomistic simulations of condensed matter systems. WIREs Comput Mol Sci 2014;4:15-25.
96. Dovesi R, Erba A, Orlando R, et al. Quantum-mechanical condensed matter simulations with CRYSTAL. WIREs Comput Mol Sci 2018;8:e1360.
97. Andolina CM, Saidi WA. Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table. Digit Discov 2023;2:1070-7.
