Deep Potentials for Materials Science

doi:10.11900/0412.1961.2024.00141

Acta Metall Sin

2024, Vol. 60

Issue (10): 1299-1311 DOI: 10.11900/0412.1961.2024.00141

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Deep Potentials for Materials Science

WEN Tongqi(

), LIU Huaiyi, GONG Xiaoguo, YE Beilin, LIU Siyu, LI Zhuoyuan

Department of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China

Cite this article:

WEN Tongqi, LIU Huaiyi, GONG Xiaoguo, YE Beilin, LIU Siyu, LI Zhuoyuan. Deep Potentials for Materials Science. Acta Metall Sin, 2024, 60(10): 1299-1311.

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Abstract

Although first-principles calculations offer high precision, they are prohibitively expensive. Conversely, molecular dynamics simulations employing classical interatomic potentials, or force fields, offer quicker but less precise outcomes. To balance between computational speed and accuracy, machine learning (ML) potential functions have been developed and have gained widespread application. The deep potential (DP) method, a type of ML potential, has attracted considerable attention recently. This paper provides a comprehensive review of DP methods in materials science. It begins with an introduction to the theoretical foundation of DP, followed by a detailed exposition of the DP model development and usage. Additionally, the application of DP in various material systems is briefly reviewed. AIS-Square contributes training databases and workflows essential for developing DP models. The paper concludes by assessing the performance of DP models relative to both first-principles calculations and classical potentials in terms of accuracy and efficiency. Finally, a brief outlook on future developments trends is provided.

Key words: deep potential atomistic simulation machine learning potential function neural network

Received: 06 May 2024

ZTFLH:

TG148

Fund: University of Hong Kong via Seed Fund(2201100392)

Corresponding Authors: WEN Tongqi, research assistant professor, Tel: (+852)97049527, E-mail: tongqwen@hku.hk

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	WEN Tongqi
	LIU Huaiyi
	GONG Xiaoguo
	YE Beilin
	LIU Siyu
	LI Zhuoyuan

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https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00141 OR https://www.ams.org.cn/EN/Y2024/V60/I10/1299

Fig.1 Learning curves of both energy (ΔE) and force (ΔF) with DeepPot-SE and DPA-1, under different setups and on different systems^[32]
(a) learning curves on the AlMgCu ternary subset, with DeepPot-SE and DPA-1 models pretrained on single-element and binary subsets
(b, c) learning curves on high-entropy alloy (HEA) (b) and AlCu (c), with DeepPot-SE (from scratch) and DPA-1 (both from scratch and pretrained on OC2M)

Fig.2 Comparative analyses of sample efficiency on downstream tasks^[33]
(a-e) RMSE convergence of energy and force predictions on FerroEle-D (a), SSE-PBESol (b), SemiCond-D (c), ANI-1x (d), H2O-PBE0TS-MD (e) tasks, respectively (RMSE—root-mean-square error)

Fig.3 Evaluations of the distilled model across various downstream applications^[33]
(a, b) comparisons of the radial distribution function (RDF) (a) and angular distribution function (ADF) (b) for the H2O-PBE0TS-MD dataset between the reference ab initio molecular dynamics (AIMD) results^[35] and the distilled mode (r_O-O—radial distribution function for O-O, ψ_O-O-O—angular distribution function for O-O-O)
(c) comparisons of diffusion constants for the solid-state electrolyte Li₁₀SnP₂S₁₂ (T—temperature)
(d, e) temperature-dependent lattice constants (a, b, and c) for the ternary solid solution ferroelectric perovskite oxides Pb(In_1/2Nb_1/2)O₃-Pb(Mg_1/3Nb_2/3)O₃-PbTiO₃ (PIN-PMN-PT) (T—tetragonal, C—cubic)
(f) computational efficiency assessment for the aforementioned three systems (N_atoms—number of atoms)

Fig.4 Comparisons of the speed of MD simulations using the compressed Ti DP, an embedded atomic potential (EAM), and/or an modified embedded atomic potential (MEAM) functions on CPU (a) and GPU (b) systems^[44]

Fig.5 Temperature-pressure phase diagram of gallium^[54]

Fig.6 Phase diagram of water^[82]
(a₁) DP model (red solid lines) and experiment (gray solid lines) for T < 420 K (P—pressure, F—fluid)
(a₂) phase diagram at high T and P
(b) phase diagram of TIP4P/2005^[83] water

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