Science Advances 2017

Machine Learning of Accurate Energy-conserving Molecular Force Fields

Science Advances 2017 stefanch/sGDML

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.