Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method

Published

November 19, 2020

Author(s)

Ganga Purja Pun, Vesselin Yamakov, James Hickman, Edward Glaessgen, Yuri Mishin

Abstract

Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) method combines a high- dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential applicable to both metals and nonmetals. In this paper, we present a modified version of the PINN method that accelerates the potential training process and further improves the transferability of PINN potentials to unknown atomic environments. As an application, a modified PINN potential for Al has been developed by training on a large database of electronic structure calculations. The potential reproduces the reference first-principles energies within 2.6 meV/atom and accurately predicts a wide spectrum of physical properties of Al. Such properties include, but are not limited to, lattice dynamics, thermal expansion, energies of point and extended defects, the melting temperature, the structure and dynamic properties of liquid Al, the surface tensions of the liquid surface and the solid-liquid interface, and the nucleation and growth of a grain boundary crack. Computational efficiency of PINN potentials is also discussed.

Citation

Physical Review Materials

Volume

4

Issue

11

Pub Type

Journals

Download Paper

https://doi.org/10.1103/PhysRevMaterials.4.113807

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Keywords

Atomistic simulations, molecular dynamics, interatomic potentials, machine learning, artificial neural networks.

Citation

Purja Pun, G. , Yamakov, V. , Hickman, J. , Glaessgen, E. and Mishin, Y. (2020), Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method, Physical Review Materials, [online], https://doi.org/10.1103/PhysRevMaterials.4.113807, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930885 (Accessed January 20, 2025)