Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …

Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic
simulations with ab initio accuracy. While constructing NN PESs, their training data points …

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies,
as it describes a chemical reaction via the reaction path and the surrounding reaction valley …

In this work, we present a general purpose deep neural network package for representing
energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called …

The construction of high-dimensional global potential energy surfaces (PESs) from ab initio
data has been a major challenge for decades. Advances in computer hardware, electronic …

One of the ultimate goals of chemistry is to understand and manipulate chemical reactions,
which implies the ability to monitor the reaction and its underlying mechanism at an atomic …

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

This work introduces a novel methodology for the quantification of uncertainties associated
with potential energy surfaces (PESs) computed from first-principles quantum mechanical …

We introduce a free and open-source software package (PES-Learn) which largely
automates the process of producing high-quality machine learning models of molecular …

Simulations of anharmonic vibrational motion rely on computationally expedient
representations of the governing potential energy surface. The n-mode representation (n …