Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion - UTU Research Portal

A1 Refereed original research article in a scientific journal

Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion

Authors: Pisal, Prajwal; Krejčí, Ondřej; Rinke, Patrick

Publisher: NATURE PORTFOLIO

Publishing place: BERLIN

Publication year: 2025

Journal: npj Computational Materials

Journal name in source: NPJ COMPUTATIONAL MATERIALS

Journal acronym: NPJ COMPUT MATER

Article number: 213

Volume: 11

Number of pages: 9

ISSN: 2096-5001

eISSN: 2057-3960

DOI: https://doi.org/10.1038/s41524-025-01664-9

Publication's open availability at the time of reporting: Open Access

Publication channel's open availability : Open Access publication channel

Web address : https://www.nature.com/articles/s41524-025-01664-9

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/499252719

Abstract

Transforming CO2 into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts, using machine-learned force fields. We propose a new catalytic descriptor, termed adsorption energy distribution, that aggregates the binding energies for different catalyst facets, binding sites, and adsorbates. The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. We propose new promising candidates such as ZnRh and ZnPt3, which to our knowledge, have not yet been tested, and discuss their possible advantage in terms of stability.

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Funding information in the publication:
This project received funding from the European Union – NextGenerationEU instrument and the Research Council of Finland's AICon project (grant number no. 348179).
Open Access funding enabled and organized by Projekt DEAL.