In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

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 a mechanistic description of molecules by relying
on empirical potentials. The quality and transferability of such potentials can be improved …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

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 …

A generalized understanding of protein dynamics is an unsolved scientific problem, the
solution of which is critical to the interpretation of the structure-function relationships that …

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 …

Collective variable-based enhanced sampling methods have been widely used to study
thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced …