The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Highly accurate ab initio molecular dynamics (MD) methods are the gold standard for
studying molecular mechanisms in the condensed phase, however, they are too expensive …

Fast and accurate calculation of intermolecular interaction energies is desirable for
understanding many chemical and biological processes, including the binding of small …

Uncertainty quantification (UQ) is important to machine learning (ML) force fields to assess
the level of confidence during prediction, as ML models are not inherently physical and can …

Multipole moments such as charge, dipole, and quadrupole are often invoked to rationalize
intermolecular phenomena, but a low-order multipole expansion is rarely a valid description …

DL_FFLUX is a force field based on quantum chemical topology that can perform molecular
dynamics for flexible molecules endowed with polarizable atomic multipole moments (up to …

Hybrid or “extended” symmetry-adapted perturbation theory (XSAPT) replaces traditional
SAPT's treatment of dispersion with better performing alternatives while at the same time …

In this study, we introduce two datasets for nanoscale noncovalent binding, featuring
complexes at the hundred-atom scale, benchmarked using coupled cluster with single …

Electronic properties and absorption spectra are the grounds to investigate molecular
electronic states and their interactions with the environment. Modeling and computations are …

FFLUX is a quantum chemical topology-based multipolar force field that uses Gaussian
process regression machine learning models to predict atomic energies and multipole …