Issue 4, 2024

Machine learning-based screening of Mn-PNP catalysts for the CO2 reduction reaction using a region-wise ligand-encoded feature matrix

Abstract

The CO2 reduction reaction is a promising way to reduce the CO2 level in the environment and most importantly to produce C1-based chemicals (HCOOH and CH3OH) that can be used as liquid fuels and industrial chemicals. In this regard, homogeneous Mn-PNP based catalysts are found to be suitable for the reaction and show promising potential for enhancing activity through ligand modification. Herein, a novel ligand encoded feature matrix enabled machine learning (ML) model has been developed to screen efficient catalysts from a large search space of earth-abundant aromatic Mn-PNP catalysts using the effects of different ligands present on the different ligand sphere of the catalysts. The ML models based on gradient boosting (GBR and XGBR) were found to be the best performing ML models with a density functional theory (DFT) level of accuracy. Potential catalysts for HCOOH and CH3OH formation are identified based on the overall reaction free energy barrier through ML + DFT. The importance of different regions (R1 and R2) and the effect of ligand substituents (+I/−I) in the catalyst are unleashed. Furthermore, a favorable mechanism to produce HCOOH has been ascertained.

Graphical abstract: Machine learning-based screening of Mn-PNP catalysts for the CO2 reduction reaction using a region-wise ligand-encoded feature matrix

Supplementary files

Article information

Article type
Paper
Submitted
26 okt 2023
Accepted
17 mar 2024
First published
18 mar 2024
This article is Open Access
Creative Commons BY-NC license

Energy Adv., 2024,3, 854-860

Machine learning-based screening of Mn-PNP catalysts for the CO2 reduction reaction using a region-wise ligand-encoded feature matrix

A. Das, D. Roy, S. C. Mandal and B. Pathak, Energy Adv., 2024, 3, 854 DOI: 10.1039/D3YA00520H

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements