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

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

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

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

In photochemical production of hydrogen from water, the hole-mediated oxidation reaction is
the rate-determining step. A poor solar-to-hydrogen efficiency is usually related to a …

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

Femtosecond nonlinear spectroscopy is the main tool for the time-resolved detection of
photophysical and photochemical processes. Since most systems of chemical interest are …

High-level quantum mechanical (QM) calculations are indispensable for accurate
explanation of natural phenomena on the atomistic level. Their staggering computational …

Over the last few decades, conical intersections (CoIns) have grown from theoretical
curiosities into common mechanistic features of photochemical reactions, whose function is …