Machine Learning Interatomic Potentials and Long-Range Physics

Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of…

• NS
  National Science Foundation

  Awards: 1818253, OAC-1818253, CHE200122, 1053575
• MU
  Multidisciplinary University Research Initiative

  Award: N00014-21-1-2476
• OO
  Office of Naval Research

  Awards: OAC-1818253, N00014-21-1-2476, N00014
• DO
  Division of Chemistry

  Awards: CHE-200122, ACI-1053575, 1053575
• DO
  Division of Advanced Cyberinfrastructure

  Awards: OAC-1818253, ACI-1053575