Machine Learning for Catalytic Materials Design and Discovery

Dear Colleagues,

Advances in machine learning have revolutionized computational materials design in numerous ways. Such advances have created new tools to design catalysts with active site detail, introduced next generation optimization methods to sample free energy surfaces, and built data mining frameworks leveraging open source data repositories. The overarching goal of these tools is to develop catalyst design principles by identifying relations between materials properties and activity, selectivity, and stability. Tremendous progress has been made in building data infrastructure and machine learning methods thereby accelerated our understanding of the functional interplay between materials properties and their catalytic behavior.

This Special Issue aims to cover recent advances in the application of machine learning in catalyst design. This theme includes, but is not limited to, high throughput techniques for large dataset generation, data mining, supervised and unsupervised learning of catalytic properties, energy correlations in catalysis, generative modeling for catalysts discovery, reinforcement, and active learning, accelerated sampling of potential/free energy surfaces, reaction pathway analysis, etc.

Dr. Osman Mamun
Prof. Dr. Tej S. Choksi
Guest Editors

Manuscript Submission Information

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