Deep Potentials for Materials Science

Abstract

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

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• Finite-temperature properties of antiferroelectric perovskite $\mathrm{PbZr}{\mathrm{O}}_3$ from deep learning interatomic potential
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• Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
• Exploring Li-ion Transport Properties of Li${}_3$TiCl${}_6$: A Machine Learning Molecular Dynamics Study
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• Size and Quality of Quantum Mechanical Data Sets for Training Neural Network Force Fields for Liquid Water
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• A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra
• Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
• Finite-temperature properties and the hidden ferroelectric $R3c$ phase of bulk CaTiO${}_3$ from second principles
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• Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials