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胡思宇 等: 基于层重组扩展卡尔曼滤波的神经网络力场训练 4107
[6] Raty JY, Gygi F, Galli G. Growth of carbon nanotubes on metal nanoparticles: A microscopic mechanism from ab initio molecular
dynamics simulations. Physical Review Letters, 95(9): 096103. [doi: 10.1103/PhysRevLett.95.096103]
[7] Ma T, Wang SH. Phase Transition Dynamics. Cham: Springer, 2019. [doi: 10.1007/978-3-030-29260-7]
[8] Johannes L, Simon S, Hans H. On the history of the Lennard-Jones potential. Annalen der Physik, 2024, (536): 2400115. [doi: 10.1002/
andp.202400115]
[9] Foiles SM, Baskes MI, Daw MS. Embedded-atom-method functions for the FCC metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys. Physical
Review B, 1986, 33(12): 7983–7991. [doi: 10.1103/physrevb.33.7983]
[10] Senftle TP, Hong S, Islam MM, Kylasa SB, Zheng YX, Shin YK, Junkermeier C, Engel-Herbert R, Janik MJ, Aktulga HM, Verstraelen
T, Grama A, van Duin ACT. The ReaxFF reactive force-field: Development, applications and future directions. npj Computational
Materials, 2016, 2(1): 15011. [doi: 10.1038/npjcompumats.2015.11]
[11] Smit B. Phase diagrams of Lennard-Jones fluids. The Journal of Chemical Physics, 1992, 96(11): 8639–8640. [doi: 10.1063/1.462271]
[12] Koura K, Matsumoto H. Variable soft sphere molecular model for inverse-power-law or Lennard-Jones potential. Physics of Fluids A,
1991, 3(10): 2459–2465. [doi: 10.1063/1.858184]
[13] Daw MS, Baskes MI. Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals. Physical
Review B, 1984, 29(12): 6443–6453. [doi: 10.1103/physrevb.29.6443]
[14] Jelinek B, Groh S, Horstemeyer MF, Houze J, Kim SG, Wagner GJ, Moitra A, Baskes MI. Modified embedded atom method potential for
Al, Si, Mg, Cu, and Fe alloys. Physical Review B, 2012, 85(24): 245102. [doi: 10.1103/physrevb.85.245102]
[15] Chenoweth K, Van Duin ACT, Goddard WA. ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation.
The Journal of Physical Chemistry A, 2008, 112(5): 1040–1053. [doi: 10.1021/jp709896w]
[16] Guo ZQ, Lu DH, Yan YJ, Hu SY, Liu RR, Tan GM, Sun NH, Jiang WR, Liu LJ, Chen YX, Zhang LF, Chen MH, Wang H, Jia WL.
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms. In: Proc. of the 27th ACM SIGPLAN Symp. on
Principles and Practice of Parallel Programming. New York: Association for Computing Machinery, 2022. 205–218. [doi: 10.1145/
3503221.3508425]
[17] Jia WL, Wang H, Chen MH, Lu DH, Lin L, Car R, Weinan E, Zhang LF. Pushing the limit of molecular dynamics with ab initio accuracy
to 100 million atoms with machine learning. In: Proc. of the 2020 Int’l Conf. for High Performance Computing, Networking, Storage and
Analysis. Atlanta: IEEE, 2020. 1–14. [doi: 10.1109/SC41405.2020.00009]
[18] Thompson AP, Swiler LP, Trott CR, Foiles SM, Tucker GJ. Spectral neighbor analysis method for automated generation of quantum-
accurate interatomic potentials. Journal of Computational Physics, 2015, 285: 316–330. [doi: 10.1016/j.jcp.2014.12.018]
[19] Lee K, Yoo D, Jeong W, Han S. SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials.
Computer Physics Communications, 2019, 242: 95–103. [doi: 10.1016/j.cpc.2019.04.014]
[20] Behler J. Representing potential energy surfaces by high-dimensional neural network potentials. Journal of Physics: Condensed Matter,
2014, 26(18): 183001. [doi: 10.1088/0953-8984/26/18/183001]
[21] Behler J. First principles neural network potentials for reactive simulations of large molecular and condensed systems. Angewandte
Chemie International Edition, 2017, 56(42): 12828–12840. [doi: 10.1002/anie.201703114]
[22] Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Physical Review
Letters, 2007, 98(14): 146401. [doi: 10.1103/physrevlett.98.146401]
[23] Yao K, Herr JE, Brown SN, Parkhill J. Intrinsic bond energies from a bonds-in-molecules neural network. The Journal of Physical
Chemistry Letters, 2017, 8(12): 2689–2694. [doi: 10.1021/acs.jpclett.7b01072]
[24] Desai S, Reeve ST, Belak JF. Implementing a neural network interatomic model with performance portability for emerging exascale
architectures. Computer Physics Communications, 2022, 270: 108156. [doi: 10.1016/j.cpc.2021.108156]
[25] Huang YP, Xia YJ, Yang LJ, Wei JC, Yang YI, Gao YQ. SPONGE: A GPU-accelerated molecular dynamics package with enhanced
sampling and AI-driven algorithms. Chinese Journal of Chemistry, 2022, 40(1): 160–168. [doi: 10.1002/cjoc.202100456]
[26] Wang H, Zhang LF, Han JQ, E WN. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular
dynamics. Computer Physics Communications, 2018, 228: 178–184. [doi: 10.1016/j.cpc.2018.03.016]
[27] Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A. Quantum-chemical insights from deep tensor neural networks. Nature
Communications, 2017, 8(1): 13890. [doi: 10.1038/ncomms13890]
[28] Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. In: Proc. of the 34th Int’l Conf.
on Machine Learning. Sydney: JMLR.org, 2017. 1263–1272.
[29] Schütt KR, Unke O, Gastegger M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In: Proc.
of the 38th Int’l Conf. on Machine Learning. PMLR, 2021. 9377–9388.

