The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Symmetry‐adapted perturbation theory (SAPT) is a well‐established method to compute
accurate intermolecular interaction energies in terms of physical effects such as …

Energy decomposition analysis (EDA) is an important class of methods to explore the nature
of interaction between fragments in a chemical system. It can decompose the interaction …

PSI4 is a free and open-source ab initio electronic structure program providing
implementations of Hartree–Fock, density functional theory, many-body perturbation theory …

In recent years, machine learning (ML) methods have become increasingly popular in
computational chemistry. After being trained on appropriate ab initio reference data, these …

We introduce a representation of any atom in any chemical environment for the automatized
generation of universal kernel ridge regression-based quantum machine learning (QML) …

The most successful and popular machine learning models of atomic-scale properties derive
their transferability from a locality ansatz. The properties of a large molecule or a bulk …

Chemists continuously harvest the power of non-covalent interactions to control phenomena
in both the micro-and macroscopic worlds. From the quantum chemical perspective, the …

Classical intermolecular potentials typically require an extensive parametrization procedure
for any new compound considered. To do away with prior parametrization, we propose a …

We present OrbNet Denali, a machine learning model for an electronic structure that is
designed as a drop-in replacement for ground-state density functional theory (DFT) energy …