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

Recently, machine learning (ML) methods have gained popularity and have performed as
powerfully predictive tools in various areas of academic and industrious activities. In …

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

Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …

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 …

The development of advanced materials is an important aspect of modern life. However, the
discovery of novel materials involves searching the vast chemical space to find materials …

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

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 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 …

Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and
materials informatics but with distinctive challenges arising from the dynamic, surface …