Neural network potentials

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Scopus citations

Abstract

Recently, artificial neural network-based methods for the construction of potential energy surfaces and molecular dynamics (MD) simulations based on them have been increasingly used in the field of theoretical chemistry. The neural network potentials (NNP) strike a good balance between accuracy and computational efficiency relative to quantum chemical calculations and MD simulations based on classical force fields. Thus, NNP is becoming a powerful tool for studying the structure and function of molecules. In this chapter, we introduce the basic theory of NNP. The construction steps and the usage of NNP are also introduced in detail with the MD simulation of methane combustion as an example. We hope that this chapter can help those readers who are new but interested in entering this field.

Original languageEnglish
Title of host publicationQuantum Chemistry in the Age of Machine Learning
PublisherElsevier
Pages279-294
Number of pages16
ISBN (Electronic)9780323900492
ISBN (Print)9780323886048
DOIs
StatePublished - 1 Jan 2022

Keywords

  • Chemical reaction
  • Molecular dynamic simulation
  • Neural network
  • Potential energy surface

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