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 …

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 …

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-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 …

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 …

Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a
molecular potential and associated force field based on the kernel ridge regression …

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 …

Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the
properties of experimentally unrealized ion combinations. However, ILs suffer from a …

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 …