Deep potential: A general representation of a many-body potential energy surface

Jiequn Han, Linfeng Zhang, Roberto Car, E. Weinan

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

We present a simple, yet general, deep neural network representation of the potential energy surface for atomic and molecular systems. It is “first-principle” based, in the sense that no ad hoc approximations or empirical fitting functions are required. When tested on a wide variety of examples, it reproduces the original model within chemical accuracy. This brings us one step closer to carrying out molecular simulations with quantum mechanics accuracy at empirical potential computational cost.

Original languageEnglish (US)
Pages (from-to)629-639
Number of pages11
JournalCommunications in Computational Physics
Volume23
Issue number3
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

Keywords

  • Deep learning
  • Molecular simulation
  • Potential energy surface

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