Advances in machine learning (ML) are making a large impact in many fields, including:
artificial intelligence, 1 materials science, 2, 3 and chemical engineering. 4 Generally, ML …

Recently, the availability of extensive catalysis-related data generated by experimental data
and theoretical calculations has promoted the development of machine learning (ML) …

Identifying the structure of heterogeneous catalysts is a critical step to model and understand
catalytic reactions and structure-property relations. Computational predictions of catalyst …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

The development of machine-learned potentials for catalyst discovery has predominantly
been focused on very specific chemistries and material compositions. While they are …

Discovering and understanding new materials with desired properties are at the heart of
materials science research, and machine learning (ML) has recently offered special …

Big data and artificial intelligence has revolutionized science in almost every field–from
economics to physics. In the area of materials science and computational heterogeneous …

The ability to forge difficult chemical bonds through catalysis has transformed society on all
fronts, from feeding the ever-growing population to increasing life expectancies through the …

The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

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 …