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

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

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

Highlights•Coarse-grained models are reduced representations of all-atom models that aim
to retain the essential molecular aspects for the system of interest.•Coarse-grained …

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 (CG) models parametrized using atomistic reference data, ie,“bottom up”
CG models, have proven useful in the study of biomolecules and other soft matter. However …

We employ deep kernel learning electronic coarse-graining (DKL-ECG) with approximate
Gaussian processes as a flexible and scalable framework for learning heteroscedastic …

Statistical (machine learning) tools for equation discovery require large amounts of data that
are typically computer generated rather than experimentally observed. Multiscale modeling …

We utilize connections between molecular coarse-graining (CG) approaches and implicit
generative models in machine learning to describe a new framework for systematic …

The most popular and universally predictive protein simulation models employ all-atom
molecular dynamics (MD), but they come at extreme computational cost. The development of …