Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion
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...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01664-9 |
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| author | Prajwal Pisal Ondřej Krejčí Patrick Rinke |
| author_facet | Prajwal Pisal Ondřej Krejčí Patrick Rinke |
| author_sort | Prajwal Pisal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ffc0c5a1776f44ecb8aab3f224c9fc30 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-ffc0c5a1776f44ecb8aab3f224c9fc302025-08-20T03:37:38ZengNature Portfolionpj Computational Materials2057-39602025-07-011111910.1038/s41524-025-01664-9Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversionPrajwal Pisal0Ondřej Krejčí1Patrick Rinke2Department of Applied Physics, Aalto UniversityDepartment of Applied Physics, Aalto UniversityDepartment of Applied Physics, Aalto UniversityAbstract 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.https://doi.org/10.1038/s41524-025-01664-9 |
| spellingShingle | Prajwal Pisal Ondřej Krejčí Patrick Rinke Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion npj Computational Materials |
| title | Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion |
| title_full | Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion |
| title_fullStr | Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion |
| title_full_unstemmed | Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion |
| title_short | Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion |
| title_sort | machine learning accelerated descriptor design for catalyst discovery in co2 to methanol conversion |
| url | https://doi.org/10.1038/s41524-025-01664-9 |
| work_keys_str_mv | AT prajwalpisal machinelearningaccelerateddescriptordesignforcatalystdiscoveryinco2tomethanolconversion AT ondrejkrejci machinelearningaccelerateddescriptordesignforcatalystdiscoveryinco2tomethanolconversion AT patrickrinke machinelearningaccelerateddescriptordesignforcatalystdiscoveryinco2tomethanolconversion |