Data science and informatics tools have been proliferating recently within the computational
materials science and catalysis fields. This proliferation has spurned the creation of various …

Materials databases generated by high-throughput computational screening, typically using
density functional theory (DFT), have become valuable resources for discovering new …

Materials chemistry is being profoundly influenced by the uptake of machine learning
methodologies. Machine learning techniques, in combination with established techniques …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …

High-throughput screening of catalysts can be performed using density functional theory
calculations to predict catalytic properties, often correlated with adsorbate binding energies …

Machine learning models can provide fast and accurate predictions of material properties
but often lack transparency. Interpretability techniques can be used with black box solutions …

Uncertainty quantification in artificial intelligence (AI)-based predictions of material
properties is of immense importance for the success and reliability of AI applications in …

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to
reveal predictive structure–property relationships. For many properties of interest in …

For high-throughput screening of materials for heterogeneous catalysis, scaling relations
provides an efficient scheme to estimate the chemisorption energies of hydrogenated …