Atomistic or ab initio molecular dynamics simulations are widely used to predict
thermodynamics and kinetics and relate them to molecular structure. A common approach to …

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular
processes on time and length scales inaccessible to all-atom simulations. Parametrizing CG …

Machine learning has recently entered into the mainstream of coarse-grained (CG)
molecular modeling and simulation. While a variety of methods for incorporating deep …

Coarse graining enables the investigation of molecular dynamics for larger systems and at
longer timescales than is possible at an atomic resolution. However, a coarse graining …

The successful recent application of machine learning methods to scientific problems
includes the learning of flexible and accurate atomic-level force-fields for materials and …

Coarse-grained (CG) molecular dynamics enables the study of biological processes at
temporal and spatial scales that would be intractable at an atomistic resolution. However …

Coarse-grained (CG) models enable highly efficient simulations of complex processes that
cannot be effectively studied with more detailed models. CG models are often parameterized …

Molecular dynamics simulations provide theoretical insight into the microscopic behavior of
condensed-phase materials and, as a predictive tool, enable computational design of new …

Coarse-grained models are a core computational tool in theoretical chemistry and
biophysics. A judicious choice of a coarse-grained model can yield physical insights by …

The ability to generate accurate coarse-grained models from reference fully atomic (or
otherwise “first-principles”) ones has become an important component in modeling the …