In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

In this Perspective, we review recent advances in constructing high-fidelity potential energy
surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs …

In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

Since its inception nearly a half century ago, CHARMM has been playing a central role in
computational biochemistry and biophysics. Commensurate with the developments in …

Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of
materials at scales beyond that accessible by ab initio methods and play an increasingly …

Dynamics of bimolecular reactions in the gas phase are of foundational importance in
combustion, atmospheric chemistry, interstellar chemistry, and plasma chemistry. These …

Quantum mechanics (QM) approaches (DFT, MP2, CCSD (T), etc.) play an important role in
calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent …

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological
processes. Accurate MD simulations require computationally demanding quantum …