Issue 5, 2023

Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

Abstract

Catalytic systems are multidimensional and still difficult to interpret even by accomplished chemists. For years high throughput experimentation has been used to find new catalysts. We describe a method to use the concept of directed evolution to synthesize new catalysts for the oxidative coupling of methane in silico via a classical genetic algorithm. The evaluation of the novel catalysts is based on predicting the C2 yield with the help of a random forest algorithm.

Graphical abstract: Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

Article information

Article type
Paper
Submitted
15 11 2022
Accepted
27 3 2023
First published
14 4 2023
This article is Open Access
Creative Commons BY-NC license

Energy Adv., 2023,2, 691-700

Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

C. L. M. von Meyenn and S. Palkovits, Energy Adv., 2023, 2, 691 DOI: 10.1039/D2YA00312K

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