[1] [1] HARUTA M. Size- and support-dependency in the catalysis of gold[J]. Catal Today, 1997, 36(1): 153-166.

[2] [2] HARUTA M, KOBAYASHI T, SANO H, et al. Novel gold catalysts for the oxidation of carbon monoxide at a temperature far below 0 ℃[J]. Chem Lett, 1987, 16(2): 405-408.

[3] [3] HARUTA M, DAT? M. Advances in the catalysis of Au nanoparticles[J]. Appl Catal Gen, 2001, 222(1/2): 427-437.

[4] [4] DAT? M, HARUTA M. Moisture effect on CO oxidation over Au/TiO2 catalyst[J]. J Catal, 2001, 201(2): 221-224.

[5] [5] MEDFORD A J, MOSES P G, JACOBSEN K W, et al. A career in catalysis: Jens kehlet n?rskov[J]. ACS Catal, 2022, 12(15): 9679-9689.

[8] [8] ZHANG X, HAN S, ZHU B, et al. Reversible loss of core-shell structure for Ni-Au bimetallic nanoparticles during CO2 hydrogenation[J]. Nat Catal, 2020, 3(4): 411-417.

[9] [9] TAO F, GRASS M E, ZHANG Y, et al. Reaction-driven restructuring of Rh-Pd and Pt-Pd core-shell nanoparticles[J]. Science, 2008, 322(5903): 932-934.

[10] [10] GAO F, WANG Y, GOODMAN D W. CO oxidation over AuPd(100) from ultrahigh vacuum to near-atmospheric pressures: The critical role of contiguous Pd atoms[J]. J Am Chem Soc, 2009, 131(16): 5734-5735.

[11] [11] LI Y, HU J, MA D, et al. Disclosure of the surface composition of TiO2-supported gold-palladium bimetallic catalysts by high-sensitivity low-energy ion scattering spectroscopy[J]. ACS Catal, 2018, 8(3): 1790-1795.

[13] [13] DAW M S, BASKES M I. Semiempirical, quantum mechanical calculation of hydrogen embrittlement in metals[J]. Phys. Rev. Lett., 1983, 50(17): 1285-1288.

[14] [14] DAW M S, BASKES M I. Embedded-atom method: derivation and application to impurities, surfaces, and other defects in metals[J]. Phys Rev B, 1984, 29(12): 6443-6453.

[15] [15] FINNIS M W, SINCLAIR J E. A simple empirical N-body potential for transition metals[J]. Philos Mag A, 1984, 50(1): 45-55.

[16] [16] BASKES M I. Modified embedded-atom potentials for cubic materials and impurities[J]. Phys Rev B, 1992, 46(5): 2727-2742.

[17] [17] LEE B J, BASKES M I. Second nearest-neighbor modified embedded-atom-method potential[J]. Phys Rev B, 2000, 62(13): 8564-8567.

[18] [18] SENFTLE T P, HONG S, ISLAM M M, et al. The ReaxFF reactive force-field: development, applications and future directions[J]. npj Comput Mater, 2016, 2(1): 15011.

[19] [19] VAN DUIN A C T, DASGUPTA S, LORANT F, et al. ReaxFF: A reactive force field for hydrocarbons[J]. J Phys Chem A, 2001, 105(41): 9396?9409.

[20] [20] CORNELL W D, CIEPLAK P, BAYLY C I, et al. A Second generation force field for the simulation of proteins, nucleic acids, and organic molecules[J]. J Am Chem Soc, 1995, 117(19): 5179-5197.

[21] [21] HORNAK V, ABEL R, OKUR A, et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters[J]. Proteins Struct Funct Bioinform, 2006, 65(3): 712-725.

[22] [22] LINDORFF-LARSEN K, PIANA S, PALMO K, et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field: Improved protein side-chain potentials[J]. Proteins Struct Funct Bioinform, 2010, 78(8): 1950-1958.

[23] [23] WANG J, WOLF R M, CALDWELL J W, et al. Development and testing of a general amber force field[J]. J Comput Chem, 2004, 25(9): 1157-1174.

[24] [24] SUN H. COMPASS: An ab initio force-field optimized for condensed-phase applications overview with details on alkane and benzene compounds[J]. J Phys Chem B, 1998, 102(38): 7338-7364.

[25] [25] ZEPEDA-RUIZ L A, STUKOWSKI A, OPPELSTRUP T, et al. Probing the limits of metal plasticity with molecular dynamics simulations[J]. Nature, 2017, 550(26): 492.

[27] [27] ZHANG L, WANG H, CAR R, et al. Phase Diagram of a deep potential water model[J]. Phys Rev Lett, 2021, 126(23): 236001.

[28] [28] KAPIL V, SCHRAN C, ZEN A, et al. The first-principles phase diagram of monolayer nanoconfined water[J]. Nature, 2022, 609(7927): 512-516.

[29] [29] NIU H, BONATI L, PIAGGI P M, et al. Ab initio phase diagram and nucleation of gallium[J]. Nat Commun, 2020, 11(1): 2654.

[30] [30] PIAGGI P M, WEIS J, PANAGIOTOPOULOS A Z, et al. Homogeneous ice nucleation in an ab initio machine-learning model of water[J]. Proc Natl Acad Sci, 2022, 119(33): e2207294119.

[31] [31] QU M, HUANG G, LIU X, et al. Room temperature bilayer water structures on a rutile TiO2 (110) surface: hydrophobic or hydrophilic?[J]. Chem Sci, 2022, 13(35): 10546-10554.

[32] [32] ZENG J, CAO L, XU M, et al. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation[J]. Nat Commun, 2020, 11(1): 5713.

[33] [33] BEHLER J. Four generations of high-dimensional neural network potentials[J]. Chem Rev, 2021, 121(16): 10037-10072.

[34] [34] MISHIN Y. Machine-learning interatomic potentials for materials science[J]. Acta Mater, 2021, 214: 116980.

[35] [35] ZHANG Y, WANG H, CHEN W, et al. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models[J]. Comput Phys Commun, 2020, 253: 107206.

[36] [36] BEHLER J. Perspective: Machine learning potentials for atomistic simulations[J]. J Chem Phys, 2016, 145(17): 170901.

[37] [37] HAN J, ZHANG L, CAR R, et al. Deep potential: A general representation of a many-body potential energy surface[J]. Commun Comput Phys, 2018, 23(3)[2021-06-01].

[38] [38] BART?K A P, PAYNE M C, KONDOR R, et al. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons[J]. Phys Rev Lett, 2010, 104(13): 136403.

[39] [39] BART?K A P, KONDOR R, CS?NYI G. On representing chemical environments[J]. Phys Rev B, 2013, 87(18): 184115.

[40] [40] RUPP M, TKATCHENKO A, M?LLER K R, et al. Fast and accurate modeling of molecular atomization energies with machine learning[J]. Phys Rev Lett, 2012, 108(5): 058301.

[41] [41] SCH?TT K T, SAUCEDA H E, KINDERMANS P J, et al. SchNet-A deep learning architecture for molecules and materials[J]. J Chem Phys, 2018, 148(24): 241722.

[42] [42] SHAPEEV A V. Moment tensor potentials: A class of systematically improvable interatomic potentials[J]. Multiscale Model Simul, 2016, 14(3): 1153-1173.

[43] [43] KHORSHIDI A, PETERSON A A. Amp: A modular approach to machine learning in atomistic simulations[J]. Comput Phys Commun, 2016, 207: 310-324.

[44] [44] SMITH J S, ISAYEV O, ROITBERG A E. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost[J]. Chem Sci, 2017, 8(4): 3192-3203.

[45] [45] WANG H, ZHANG L, HAN J, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics[J]. Comput Phys Commun, 2018, 228: 178-184.

[46] [46] SINGRABER A, BEHLER J, DELLAGO C. Library-based LAMMPS implementation of high-dimensional neural network potentials[J]. J Chem Theory Comput, 2019, 15(3): 1827-1840.

[47] [47] KOLB B, LENTZ L C, KOLPAK A M. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods[J]. Sci Rep, 2017, 7(1): 1192.

[48] [48] SCH?TT K T, KESSEL P, GASTEGGER M, et al. SchNetPack: A deep learning toolbox for atomistic systems[J]. J Chem Theory Comput, 2019, 15(1): 448-455.

[49] [49] YAO K, HERR J E, TOTH D W, et al. The TensorMol-0.1 model chemistry: A neural network augmented with long-range physics[J]. Che. Sc., 2018, 9(8): 2261-2269.

[50] [50] DOERR S, MAJEWSKI M, P?REZ A, et al. TorchMD: A deep learning framework for molecular simulations[J]. J Chem Theory Comput, 2021, 17(4): 2355?2363.

[51] [51] ARTRITH N, URBAN A. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2[J]. Comput Mater Sci, 2016, 114: 135-150.

[52] [52] LI L, LI H, SEYMOUR I D, et al. Pair-distribution-function guided optimization of fingerprints for atom-centered neural network potentials[J]. J Chem Phys, 2020, 152(22): 224102.

[54] [54] BEHLER J, PARRINELLO M. Generalized neural-network representation of high-dimensional potential-energy surfaces[J]. Phys Rev Lett, 2007, 98(14): 146401.

[55] [55] BEHLER J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials[J]. J Chem Phys, 2011, 134(7): 074106.

[56] [56] BEHLER J. Constructing high-dimensional neural network potentials: A tutorial review[J]. Int J Quantum Chem, 2015, 115(16): 1032-1050.

[57] [57] ZHANG L, HAN J, WANG H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics[J]. Phys Rev Lett, 2018, 120(14): 143001.

[58] [58] ZHANG L, HAN J, WANG H, et al. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems[C]//Montréal, Canada: arXiv:1805.09003, 2018.

[59] [59] MILLS G, J?NSSON H. Quantum and thermal effects in H2 dissociative adsorption: Evaluation of free energy barriers in multidimensional quantum systems[J]. Phys Rev Lett, 1994, 72(7): 1124?1127.

[60] [60] MILLS G, J?NSSON H, SCHENTER G K. Reversible work transition state theory: application to dissociative adsorption of hydrogen[J]. Surf Sci, 1995, 324(2/3): 305-337.

[61] [61] HENKELMAN G, J?NSSON H. Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points[J]. J Chem Phys, 2000, 113(22): 9978-9985.

[62] [62] HENKELMAN G, UBERUAGA B P, J?NSSON H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths[J]. J Chem Phys, 2000, 113(22): 9901-9904.

[63] [63] K?HNE T D, IANNUZZI M, DEL BEN M, et al. CP2K: An electronic structure and molecular dynamics software package- quickstep: Efficient and accurate electronic structure calculations[J]. J Chem Phys, 2020, 152(19): 194103.

[64] [64] BRANDENBURG J G, ZEN A, ALF? D, et al. Interaction between water and carbon nanostructures: How good are current density functional approximations?[J]. J Chem Phys, 2019, 151(16): 164702.

[65] [65] FREY H, BECK A, HUANG X, et al. Dynamic interplay between metal nanoparticles and oxide support under redox conditions[J]. Science, 2022, 376(6596): 982-987.

[66] [66] TESCHNER D, BORSODI J, WOOTSCH A, et al. The roles of subsurface carbon and hydrogen in palladium-catalyzed alkyne hydrogenation[J]. Science, 2008, 320(5872): 86-89.

[67] [67] SANKAR M, DIMITRATOS N, MIEDZIAK P J, et al. Designing bimetallic catalysts for a green and sustainable future[J]. Chem Soc Rev, 2012, 41(24): 8099.

[68] [68] MICHAELIDES I N, DIXON D J. Catalytic stereoselective semihydrogenation of alkynes to E-alkenes[J]. Angew Chem Int Ed, 2013, 52(3): 806-808.

[69] [69] WANG Y, LE J, LI W, et al. In situ spectroscopic insight into the origin of the enhanced performance of bimetallic nanocatalysts towards the oxygen reduction reaction (ORR)[J]. Angew Chem Int Ed, 2019, 58(45): 16062-16066.

[70] [70] SUN Q, WANG N, FAN Q, et al. Subnanometer bimetallic platinum-zinc clusters in zeolites for propane dehydrogenation[J]. Angew Chem Int Ed, 2020, 59(44): 19450-19459.

[71] [71] LIU S, LI Y, YU X, et al. Tuning crystal-phase of bimetallic single-nanoparticle for catalytic hydrogenation[J]. Nat Commun, 2022, 13(1): 4559.

[72] [72] GUAN Q, ZHU C, LIN Y, et al. Bimetallic monolayer catalyst breaks the activity-selectivity trade-off on metal particle size for efficient chemoselective hydrogenations[J]. Nat Catal, 2021, 4(10): 840-849.

[73] [73] PALMER C, UPHAM D C, SMART S, et al. Dry reforming of methane catalysed by molten metal alloys[J]. Nat Catal, 2020, 3(1): 83-89.

[74] [74] LIU S, LI Y, YU X, et al. Tuning crystal-phase of bimetallic single-nanoparticle for catalytic hydrogenation[J]. Nat Commun, 2022, 13(1): 4559.

[75] [75] STUDT F, ABILD-PEDERSEN F, BLIGAARD T, et al. Identification of non-precious metal alloy catalysts for selective hydrogenation of acetylene[J]. Science, 2008, 320(5881): 1320-1322.

[76] [76] KYRIAKOU G, BOUCHER M B, JEWELL A D, et al. Isolated metal atom geometries as a strategy for selective heterogeneous hydrogenations[J]. Science, 2012, 335(6073): 1209-1212.

[77] [77] SONG Y, OZDEMIR E, RAMESH S, et al. Dry reforming of methane by stable Ni-Mo nanocatalysts on single-crystalline MgO[J]. Science, 2020, 367(6479): 777-781.

[78] [78] ZHANG H, WATANABE T, OKUMURA M, et al. Catalytically highly active top gold atom on palladium nanocluster[J]. Nat Mater, 2012, 11(1): 49-52.

[79] [79] CHEN M, KUMAR D, YI C W, et al. The promotional effect of gold in catalysis by palladium-gold[J]. Science, 2005, 310(5746): 291-293.

[80] [80] ZHOU C, NGAN H T, LIM J S, et al. Dynamical study of adsorbate-induced restructuring kinetics in bimetallic catalysts using the PdAu(111) model system[J]. J Am Chem Soc, 2022, 144(33): 15132-15142.

[81] [81] GARZA R B, LEE J, NGUYEN M H, et al. Atomistic mechanisms of binary alloy surface segregation from nanoseconds to seconds using accelerated dynamics[J]. J Chem Theory Comput, 2022, 18(7): 4447-4455.

[82] [82] HENKELMAN G, J?NSSON H. Long time scale kinetic Monte Carlo simulations without lattice approximation and predefined event table[J]. J Chem Phys, 2001, 115(21): 9657-9666.

[83] [83] XU L, HENKELMAN G. Adaptive kinetic Monte Carlo for first-principles accelerated dynamics[J]. J Chem Phys, 2008, 129(11): 114104.

[84] [84] LI L, LI X, DUAN Z, et al. Adaptive kinetic Monte Carlo simulations of surface segregation in PdAu nanoparticles[J]. Nanoscale, 2019, 11(21): 10524-10535.

[85] [85] LI X, WANG C, TANG J. Methane transformation by photocatalysis[J]. Nat Rev Mater, 2022, 7(8): 617-632.

[86] [86] B?LLER B, DURNER K M, WINTTERLIN J. The active sites of a working Fischer-Tropsch catalyst revealed by operando scanning tunnelling microscopy[J]. Nat Catal, 2019, 2(11): 1027?1034.

[87] [87] LI J, HE Y, TAN L, et al. Integrated tuneable synthesis of liquid fuels via Fischer-Tropsch technology[J]. Nat Catal, 2018, 1(10): 787-793.

[88] [88] LI L, ZENG X C. Direct simulation evidence of generation of oxygen vacancies at the golden cage Au16 and TiO2 (110) interface for CO oxidation[J]. J Am Chem Soc, 2014, 136(45): 15857-15860.

[89] [89] WANG Y G, MEI D, GLEZAKOU V A, et al. Dynamic formation of single-atom catalytic active sites on ceria-supported gold nanoparticles[J]. Nat Commun, 2015, 6(1): 6511.

[90] [90] LI P, JIANG Y, HU Y, et al. Hydrogen bond network connectivity in the electric double layer dominates the kinetic pH effect in hydrogen electrocatalysis on Pt[J]. Nat Catal, 2022, 5(10): 900-911.

[91] [91] GALIB M, LIMMER D T. Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial processes[J]. Science, 2021, 371(6532): 921-925.

[92] [92] LAN J, V. RYBKIN V, PASQUARELLO A. Evolution of Aqueous Electron with Varying Temperature[R]. Chemistry, 2022.