This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from
energy storage to solvents, where they have been touted as “designer solvents” as they can …

To design new materials and understand their novel phenomena, it is imperative to predict
the structure and properties of materials that often rely on first-principles theory. However …

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 …

As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …