Finding low-energy molecular conformers is challenging due to the high dimensionality of
the search space and the computational cost of accurate quantum chemical methods for …

An overview of computational methods to describe high-dimensional potential energy
surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy …

Lack of quality data and difficulty generating these data hinder quantitative understanding of
reaction kinetics. Specifically, conventional methods to generate transition state structures …

Modern machine learning force fields (ML-FF) are able to yield energy and force predictions
at the accuracy of high-level ab initio methods, but at a much lower computational cost. On …

Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the
inclusion of the zero point energy and its coupling with the anharmonicities in interatomic …

Machine learning (ML) empowers biomedical systems with the capability to optimize their
performance through modeling of the available data extremely well, without using strong …

Gaussian process (GP) regression has been recently developed as an effective method in
molecular geometry optimization. The prior mean function is one of the crucial parts of the …

Recent developments in machine learning interatomic potentials (MLIPs) have empowered
even nonexperts in machine learning to train MLIPs for accelerating materials simulations …

Molecular dynamics (MD) simulations are a cornerstone in science, enabling the
investigation of a system's thermodynamics all the way to analyzing intricate molecular …

In recent years, the prediction of quantum mechanical observables with machine learning
methods has become increasingly popular. Message-passing neural networks (MPNNs) …