Refining potential energy surface through dynamical properties via differentiable molecular simulation.

ABSTRACT: Recently, machine learning potential (MLP) largely enhances the reliability of molecular dynamics, but its accuracy is limited by the underlying ab initio methods. A viable approach to overcome this limitation is to refine the potential by learning from experimental data, which now can be done efficiently using modern automatic differentiation technique. However, potential refinement is mostly performed using thermodynamic properties, leaving the most accessible and informative dynamical data (like spectroscopy) unexploited. In this work, through a comprehensive application of adjoint and gradient truncation methods, we show that both memory and gradient explosion issues can be circumvented in many situations, so the dynamical property differentiation is well-behaved. Consequently, both tr

SUBMITTER: Han B 

PROVIDER: S-EPMC11742923 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature