Low-dimensional hybrid organic–inorganic perovskites (HOIPs) are promising electronically
active materials for light absorption and emission. The design space of HOIPs is extremely …

A wide range of density functional methods and basis sets are available to derive the
electronic structure and properties of molecules. Quantum mechanical calculations are too …

Vibrational spectroscopy is a cornerstone technique for molecular characterization and
offers an ideal target for the computational investigation of molecular materials. Building on …

Density functional theory (DFT) has been a cornerstone in computational science, providing
powerful insights into structure–property relationships for molecules and materials through …

We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations
for training machine learning potentials. It expands on the original data set by adding much …

Force fields are a key component of physics-based molecular modeling, describing the
energies and forces in a molecular system as a function of the positions of the atoms and …

We develop a framework for on-the-fly machine learned force field molecular dynamics
simulations based on the multipole featurization scheme that overcomes the bottleneck with …

Machine learning (ML) methods offer a promising route to the construction of universal
molecular potentials with high accuracy and low computational cost. It is becoming evident …

The molecular force field (FF) determines the accuracy of molecular dynamics (MD) and is
one of the major bottlenecks that limits the application of MD in molecular design. Recently …

Machine-learned potentials (MLPs) trained on ab initio data combine the computational
efficiency of classical interatomic potentials with the accuracy and generality of the first …