Volume 1 Issue 2
June  2022
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Tongqi Wen, Linfeng Zhang, Han Wang, Weinan E, David J Srolovitz. Deep potentials for materials science[J]. Materials Futures, 2022, 1(2): 022601. doi: 10.1088/2752-5724/ac681d
Citation: Tongqi Wen, Linfeng Zhang, Han Wang, Weinan E, David J Srolovitz. Deep potentials for materials science[J]. Materials Futures, 2022, 1(2): 022601. doi: 10.1088/2752-5724/ac681d
shu
Topical Review •
OPEN ACCESS

Deep potentials for materials science

© 2022 The Author(s). Published by IOP Publishing Ltd on behalf of the Songshan Lake Materials Laboratory
Materials Futures, Volume 1, Number 2
  • Received Date: 2022-02-28
  • Accepted Date: 2022-04-19
  • Rev Recd Date: 2022-04-15
  • Publish Date: 2022-05-11
doi: 10.1088/2752-5724/ac681d
  • 1.

    Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region of China

  • 2.

    DP Technology, Beijing, People’s Republic of China

  • 3.

    AI for Science Institute, Beijing, People’s Republic of China

  • 4.

    Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, People’s Republic of China

  • 5.

    HEDPS, CAPT, College of Engineering, Peking University, Beijing, People’s Republic of China

  • 6.

    School of Mathematical Sciences, Peking University, Beijing, People’s Republic of China

  • 7.

    Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, United States of America

  • 8.

    International Digital Economy Academy (IDEA), Shenzhen, People’s Republic of China

  • Authors to whom any correspondence should be addressed.

More Information
    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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