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

Machine-learned coarse-grained (CG) models have the potential for simulating large
molecular complexes beyond what is possible with atomistic molecular dynamics. However …

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 …

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 …

Over the last few years, coarse-grained molecular dynamics has emerged as a way to model
large and complex systems in an efficient and inexpensive manner due to its lowered …

Large-scale computational molecular models provide scientists a means to investigate the
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …

Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their
many-body capacity allows highly accurate approximations of the potential of mean force …

Coarse-grained (CG) simulation models have become very popular tools to study complex
molecular systems with great computational efficiency on length and time scales that are …

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 …