Machine learning of accurate energy-conserving molecular force fields.

ABSTRACT: Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol^-1 for energies and 1 kcal mol^-1 Å̊^-1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accom

SUBMITTER: Chmiela S 

PROVIDER: S-EPMC5419702 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature