Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

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 …

We propose a simple, but efficient and accurate, machine learning (ML) model for
developing a high-dimensional potential energy surface. This so-called embedded atom …

With advances in ab initio theory, it is now possible to calculate electronic energies within
chemical (< 1 kcal/mol) accuracy. However, it is still challenging to represent faithfully a …

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 …

A simple, general, and rigorous scheme for adapting permutation symmetry in molecular
systems is proposed and tested for fitting global potential energy surfaces using neural …

The development of interatomic potentials employing artificial neural networks has seen
tremendous progress in recent years. While until recently the applicability of neural network …

Quantitative simulations of electronically nonadiabatic molecular processes require both
accurate dynamics algorithms and accurate electronic structure information. Direct …

A more flexible neural network (NN) method using the fundamental invariants (FIs) as the
input vector is proposed in the construction of potential energy surfaces for molecular …