An AI-for-Science Platform of Molecular Dynamics with Ab initio Accuracy

doi:10.11871/jfdc.issn.2096-742X.2023.03.002

Abstract

Abstract:

[Objective] AI for Science is changing the landscape of the traditional scientific computing by combining physical models, artificial intelligence, and high-performance computing to address challenging problems such as molecular dynamics with ab initio accuracy. This approach adapts neural networks to fitting high-dimensional functions, achieving orders of magnitude increases in the temporal and spatial scales, leading to a paradigm shift in scientific research. [Methods] This paper proposes an HPC+AI-driven computing platform for molecular dynamics with ab initio accuracy. Aiming at the changes and challenges brought by the workflow, the key technologies and processes for building an AI for Science computing platform are described from four aspects: generating scientific data and preparing datasets, exploring configuration space and labeling training samples, efficiently training AI for Science models, and performing large scale efficient inference (MD simulation). [Results] Based on the computational platform proposed in this paper and AI for Science computing workflows, this paper proposes an active learning strategy based on Kalman filtering for the typical application of HPC+AI-driven first-principles accuracy molecular dynamics. The training method for the quasi-second-order AI model is improved, achieving a training time acceleration from days to minutes. A fifth-order polynomial model compression technology increases the system scale by one order of magnitude for model inference and accelerates time-to-solution by 3-9 times. [Conclusions] All of the above work is combined to form an AI for Science computing platform for first-principles accuracy molecular dynamics calculations. [Limitations and Prospects] The AI for Science computing approach and workflows are still in a vigorous stage of development and facing significant challenges in high-precision data, more general AI models, and efficient computing methods. These challenges will also be important directions for future exploration in this work.

Key words: AI for Science, first-principles calculation, molecular dynamics, active learning, Kalman Filtering, Model compression

LIU Tao, ZHAO Tong, TAN Guangming, JIA Weile. An AI-for-Science Platform of Molecular Dynamics with Ab initio Accuracy[J]. Frontiers of Data and Computing, 2023, 5(3): 13-28, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.03.002.

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物理量	$\mathrm{V}_{0}\left(Å^{3} / \text { atom }\right)$	E_Vf(eV)	a₀(Å)
DFT	41.6892	0.0002	3.4674
ALKPU	41.2217	0.0016	3.4544