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 …

Unleashing the predictive power of molecular dynamics (MD), Neural Network Potentials
(NNPs) trained on Density Functional Theory (DFT) calculations are revolutionizing our …

Computational investigations of biological and soft-matter systems governed by strongly
anisotropic interactions typically require resource-demanding methods such as atomistic …

Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic
particles with molecular dynamics or Monte Carlo methods. To accurately represent the …

Understanding the relationship between multiscale morphology and electronic structure is a
grand challenge for semiconducting soft materials. Computational studies aimed at …

The MSCG/FM (multiscale coarse-graining via force-matching) approach is an efficient
supervised machine learning method to develop microscopically informed coarse-grained …

Electrolyte solutions play critical role in a vast range of important applications, yet an
accurate and scalable method of predicting their properties without fitting to experiment has …

With the aim of producing realistic coarse-grained models of homopolymers, we introduce a
tabulated backbone-oriented anisotropic potential. The parameters of the model are …