We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …

We developed a web application structured in a machine learning and molecular fingerprint
algorithm for the automatic calculation of the reaction rate constant of the oxidative …

Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …

Abstract Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry
of a given graph, such as molecular dynamics simulation. While incorporating Euclidean …

Finding low-energy molecular conformers is challenging due to the high dimensionality of
the search space and the computational cost of accurate quantum chemical methods for …

A machine-learning based approach for evaluating potential energies for quantum
mechanical studies of properties of the ground and excited vibrational states of small …

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