By averaging over atomic details, coarse-grained (CG) models provide profound
computational and conceptual advantages for studying soft materials. In particular, bottom …

Low-resolution coarse-grained (CG) models provide remarkable computational and
conceptual advantages for simulating soft materials. In principle, bottom-up CG models can …

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 …

Coarse-grained models allow computational investigation of biomolecular processes
occurring on long time and length scales, intractable with atomistic simulation. Traditionally …

The widespread application of machine learning (ML) to the chemical sciences is making it
very important to understand how the ML models learn to correlate chemical structures with …

Machine learning has had a significant impact on multiple areas of science, technology,
health, and computer and information sciences. Through the advent of quantum computing …

Machine-learning methods have gained significant attention in the computational chemistry
community as a viable approach to molecular modeling and analysis. Recent successes in …

A plethora of key biological events occur at the cellular membrane where the large
spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches …

Graph neural network (GNN) architectures have emerged as promising force field models,
exhibiting high accuracy in predicting complex energies and forces based on atomic …