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Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics.

Chenghan LiGregory A Voth
Published in: Journal of chemical theory and computation (2022)
Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest-scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a 2 orders of magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.
Keyphrases
  • molecular dynamics
  • density functional theory
  • machine learning
  • early onset
  • artificial intelligence
  • smooth muscle