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

Halide perovskites (HaPs) have emerged as promising new materials for a wide range of
optoelectronic applications, notably solar energy conversion. These materials are well …

Obtaining microscopic structure-property relationships for grain boundaries is challenging
due to their complex atomic structures. Recent efforts use machine learning to derive these …

Long-range correlations are essential across numerous machine learning tasks, especially
for data embedded in Euclidean space, where the relative positions and orientations of …

Calculating anharmonic vibrational modes of molecules for interpreting experimental
spectra is one of the most interesting challenges of contemporary computational chemistry …

Machine learning interatomic potentials (MLIPs) are rapidly gaining interest for molecular
modeling, as they provide a balance between quantum-mechanical level descriptions of …

Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins,
and materials on wide time and length scales. Their reliability and predictiveness, however …

DFT-based machine-learning potentials (MLPs) are now routinely trained for condensed-
phase systems, but surpassing DFT accuracy remains challenging due to the cost or …