Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

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 …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …

Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …

Physical catalysts often have multiple sites where reactions can take place. One prominent
example is single-atom alloys, where the reactive dopant atoms can preferentially locate in …

Chemical compound space refers to the vast set of all possible chemical compounds,
estimated to contain 1060 molecules. While intractable as a whole, modern machine …

Elemental phosphorus is attracting growing interest across fundamental and applied fields
of research. However, atomistic simulations of phosphorus have remained an outstanding …

Thermal transport properties of amorphous materials are crucial for their emerging
applications in energy and electronic devices. However, understanding and controlling …

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry.
Gaussian processes have long been a cornerstone of probabilistic machine learning …