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 …

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 recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

For molecules with more than three atoms, it is difficult to fit or interpolate a potential energy
surface (PES) from a small number of (usually ab initio) energies at points. Many methods …

Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

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 …

We present an efficient approach for generating highly accurate molecular potential energy
surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine …

Abstract Machine learning (ML) methods have recently been increasingly widely used in
quantum chemistry. While ML methods are now accepted as high accuracy approaches to …

Abstract Knowledge of intensities of rovibrational transitions of various molecules and theirs
isotopic species in wide spectral and temperature ranges is essential for the modeling of …

In this paper, a novel methodology is presented for the construction of ab initio effective
rotation–vibration spectroscopic models from potential energy and dipole moment surfaces …