Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

There has been great progress in develo** machine-learned potential energy surfaces
(PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of …

“Δ-machine learning” refers to a machine learning approach to bring a property such as a
potential energy surface (PES) based on low-level (LL) density functional theory (DFT) …

Duo is a general, user-friendly program for computing rotational, rovibrational and rovibronic
spectra of diatomic molecules. Duo solves the Schrödinger equation for the motion of the …

In the early days of computation, slow processor speeds limited the amount of data that
could be generated and used for scientific purposes. In the age of big data, the limiting factor …

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

The structure and dynamics of a molecular system is governed by its potential energy
surface (PES), representing the total energy as a function of the nuclear coordinates …

Feshbach resonances are fundamental to interparticle interactions and become particularly
important in cold collisions with atoms, ions, and molecules. In this work, we present the …

The combination of transfer learning (TL) a low-level potential energy surface (PES) to a
higher level of electronic structure theory together with ring-polymer instanton (RPI) theory is …

This review deals with the relation between intermolecular potentials and the spectra of van
der Waals molecules. Intermolecular potentials cannot be measured directly, but the …