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 …

In chemistry and physics, machine learning (ML) methods promise transformative impacts by
advancing modeling and improving our understanding of complex molecules and materials …

The design of molecular catalysts typically involves reconciling multiple conflicting property
requirements, largely relying on human intuition and local structural searches. However, the …

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 …

Physically motivated and mathematically robust atom-centered representations of molecular
structures are key to the success of modern atomistic machine learning. They lie at the …

Data-driven schemes that associate molecular and crystal structures with their microscopic
properties share the need for a concise, effective description of the arrangement of their …

Interaction analysis techniques, including the many-body expansion (MBE), symmetry-
adapted perturbation theory, and energy decomposition analysis, allow for an intuitive …

In this work, we explore the quantum chemical foundations of descriptors for molecular
similarity. Such descriptors are key for traversing chemical compound space with machine …

Selecting the most relevant features and samples out of a large set of candidates is a task
that occurs very often in the context of automated data analysis, where it improves the …

Real-world data typically contain a large number of features that are often heterogeneous in
nature, relevance, and also units of measure. When assessing the similarity between data …