Okello, Felix, Manda, Timothy, Ochilo, Livingstone, Okumu, Fred, Omwoma, Solomon, Magero, Denis, Pembere, Anthony. (1404). Screening of Metal Catalysts for CO2 Conversion via Machine Learning and Molecular Simulations. هنرهای کاربردی, 5(2), 97-106. doi: 10.22075/ppam.2025.36704.1130
Felix Okello; Timothy Manda; Livingstone Ochilo; Fred Okumu; Solomon Omwoma; Denis Magero; Anthony Pembere. "Screening of Metal Catalysts for CO2 Conversion via Machine Learning and Molecular Simulations". هنرهای کاربردی, 5, 2, 1404, 97-106. doi: 10.22075/ppam.2025.36704.1130
Okello, Felix, Manda, Timothy, Ochilo, Livingstone, Okumu, Fred, Omwoma, Solomon, Magero, Denis, Pembere, Anthony. (1404). 'Screening of Metal Catalysts for CO2 Conversion via Machine Learning and Molecular Simulations', هنرهای کاربردی, 5(2), pp. 97-106. doi: 10.22075/ppam.2025.36704.1130
Okello, Felix, Manda, Timothy, Ochilo, Livingstone, Okumu, Fred, Omwoma, Solomon, Magero, Denis, Pembere, Anthony. Screening of Metal Catalysts for CO2 Conversion via Machine Learning and Molecular Simulations. هنرهای کاربردی, 1404; 5(2): 97-106. doi: 10.22075/ppam.2025.36704.1130

Screening of Metal Catalysts for CO2 Conversion via Machine Learning and Molecular Simulations

Progress in Physics of Applied Materials
مقاله 1، دوره 5، شماره 2 - شماره پیاپی 9، بهمن 2025، صفحه 97-106    اصل مقاله (1.08 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22075/ppam.2025.36704.1130
نویسندگان
Felix Okello1؛ Timothy Manda1؛ Livingstone Ochilo1؛ Fred Okumu1؛ Solomon Omwoma1؛ Denis Magero2؛ Anthony Pembere* 1
1Department of Physical Science, Jaramogi Oginga Odinga University of Science and Technology, P.O. Box 210, Bondo, Kenya
2Alupe University P.O. Box 845 Busia- Kenya 50400
چکیده
This study's primary objective is to improve catalyst discovery by assessing earth-abundant metal catalysts for the conversion of CO2 to methane through the use of machine learning (ML) and molecular dynamics (MD) simulations. The highest CO2 binding energy on 61 metals was determined to be -9.75 eV for nickel (Ni), -8.7 eV for copper (Cu), and -7.75 eV for carbon (C). Various ML models were developed to predict binding energies on the metallic surfaces. Easily accessible properties of the metals and features obtained from molecular simulations were used as input features. RANSACRegressor, LinearSVR, HuberRegressor, OrthogonalMatchingPursuit CV, and LarsCV models exhibited high prediction accuracy with R-squared values of 0.99 and RMSE ranging from 0.18 to 0.40. Feature significance analysis revealed that density (D) is among the most significant structural features affecting binding energy. This work offers a dependable, high-throughput method for identifying efficient CO2 conversion catalysts, advancing sustainable technologies.
کلیدواژه‌ها
Metal Catalysts؛ CO2 Conversion؛ Machine Learning؛ Molecular Simulations
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