sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus Muller, Alexandre Tkatchenko

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.

Original languageEnglish
JournalComputer Physics Communications
DOIs
Publication statusPublished - 2019 Jan 1

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Keywords

  • Ab initio molecular dynamics
  • Coupled cluster calculations
  • Gradient domain machine learning
  • Machine learning force field
  • Machine learning potential
  • Molecular property prediction
  • Path integral molecular dynamics
  • Quantum chemistry

ASJC Scopus subject areas

  • Hardware and Architecture
  • Physics and Astronomy(all)

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