Machine learning of accurate energy-conserving molecular force fields

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 accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

• DF
  Deutsche Forschungsgemeinschaft

  Awards: MU 987/20-1, ID0EVBAI16416
• MO
  Ministry of Education, Science and Technology

  Awards: ID0EQGAI16417, 2012-005741