Abstract Machine learning has the potential to revolutionize the field of molecular simulation
through the development of efficient and accurate models of interatomic interactions. Neural …

By adopting a perspective informed by contemporary liquid-state theory, we consider how to
train an artificial neural network potential to describe inhomogeneous, disordered systems …

This review focuses on the striking recent progress in solving for hydrophobic interactions
between small inert molecules. We discuss several new understandings. First, the inverse …

Coulomb interactions play a crucial role in a wide array of processes in aqueous solutions
but present conceptual and computational challenges to both theory and simulations. We …

Machine-learning force fields have achieved significant strides in accurately reproducing the
potential energy surface with quantum chemical accuracy. However, this approach still faces …

A thorough exploration of the molecular basis for hydrophobicity with a comprehensive set of
theoretical tools and an extensive set of organic solvent S/water binary systems is discussed …

In classical density functional theory (DFT), the part of the Helmholtz free energy functional
arising from attractive inter-particle interactions is often treated in a mean-field or van der …

RNA molecules cannot fold in the absence of counterions. Experiments are typically
performed in the presence of monovalent and divalent cations. How to treat the impact of a …

The recent random batch Ewald algorithm, originating from a stochastic approximation,
performs 1 order of magnitude faster than the mainstream algorithms such as the particle …

The dielectric nature of polar liquids underpins much of their ability to act as useful solvents,
but its description is complicated by the long-ranged nature of dipolar interactions. This is …