Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential

Nikita Rybin; Ivan S. Novikov; Alexander Shapeev

Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential

Materials Science 2024-10-07 v1 Soft Condensed Matter

Authors: Nikita Rybin , Ivan S. Novikov , Alexander Shapeev

Abstract

Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure.

Keywords

crystal structure prediction machine learning for materials science molecular dynamics simulations

Cite

@article{arxiv.2410.03484,
  title  = {Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential},
  author = {Nikita Rybin and Ivan S. Novikov and Alexander Shapeev},
  journal= {arXiv preprint arXiv:2410.03484},
  year   = {2024}
}

Related papers

View all related →