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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Catalysts is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Computational Catalysis
• Molecular Modeling
• Catalytic properties
• Machine Learning
• Catalysis Informatics