In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …

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

All-atom molecular simulations offer detailed insights into macromolecular phenomena, but
their substantial computational cost hinders the exploration of complex biological processes …

Coarse-grained models have become ubiquitous in biomolecular modeling tasks aimed at
studying slow dynamical processes such as protein folding and DNA hybridization. These …

Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for
modeling biomolecules. However, MLCG models currently require large amounts of data …

This review highlights recent advances in AI-driven methods for generating Boltzmann-
weighted structural ensembles, which are crucial for understanding biomolecular dynamics …

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

Following the sequence and structure revolutions, predicting the dynamical mechanisms of
proteins that implement biological function remains an outstanding scientific challenge …

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab
initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations …

The generation of equilibrium samples of molecular systems has been a long-standing
problem in statistical physics. Boltzmann Generators are a generative machine learning …