Issue 10, 2024

Analysis of photocatalytic CO2 reduction over MOFs using machine learning

Abstract

Photocatalytic CO2 reduction over metal–organic frameworks (MOFs) is investigated by constructing a database from published articles and analyzed using machine learning tools to predict the total gas product yield (random forest regression) and predominant product types under various conditions (decision tree classification). Hyperparameters of the random forest model, ntree (120) and mtry (14) are optimized by 5-fold cross validation leading to R2 values of 0.96, 0.94 and 0.60 for training, validation and testing, respectively indicating the predictive power of the model developed. Reactor volume, sacrificial agent and amount of catalyst per reaction volume were the most important variables for total gas production rate prediction. Decision tree models, developed for gas phase and liquid phase systems separately, to determine the predominant product types (CO or CH4 in the gas phase, and one of CH3OH, CO, H2 and HCOOH in the liquid phase) depending on the photocatalyst properties and reaction conditions, were also successful with an overall testing accuracy of 87% and 77% for gas-phase and liquid-phase processes, respectively.

Graphical abstract: Analysis of photocatalytic CO2 reduction over MOFs using machine learning

Supplementary files

Article information

Article type
Paper
Submitted
13 Nov. 2023
Accepted
31 Janv. 2024
First published
01 Febr. 2024

J. Mater. Chem. A, 2024,12, 5748-5759

Analysis of photocatalytic CO2 reduction over MOFs using machine learning

S. Özsoysal, B. Oral and R. Yıldırım, J. Mater. Chem. A, 2024, 12, 5748 DOI: 10.1039/D3TA07001H

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