Computational screening in heterogeneous catalysis relies increasingly on machine
learning models for predicting key input parameters due to the high cost of computing these …

Machine learning has recently emerged as an efficient and powerful alternative to density
functional theory for studying heterogeneous catalysis. Machine learning methods rely on a …

Machine learning has been successfully applied in recent years to screen materials for a
variety of applications. However, despite recent advances, most screening-based machine …

Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale
factors, ranging from the catalysts' local morphology to the presence of high adsorbate …

Heterogeneous catalysis, for its industrial importance and great complexity in structure, has
long been the testing ground of new characterization techniques. Machine learning (ML) as …

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

Heterogeneous catalysts constitute a crucial component of many industrial processes, and
to gain an understanding of the atomic-scale features of such catalysts, ab initio density …

Conspectus Heterogeneous catalysts are rather complex materials that come in many
classes (eg, metals, oxides, carbides) and shapes. At the same time, the interaction of the …

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

The binding site and energy is an invaluable descriptor in high-throughput screening of
catalysts, as it is accessible and correlates with the activity and selectivity. Recently …