Fusing a machine learning strategy with density functional theory to hasten the discovery of 2D MXene-based catalysts for hydrogen generation-Reference-Cited by-同舟云学术

Fusing a machine learning strategy with density functional theory to hasten the discovery of 2D MXene-based catalysts for hydrogen generation

Published:2023 Issue:15 Volume:11 Page:8091-8100
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Abraham B. Moses¹^ORCID,Sinha Priyanka¹,Halder Prosun¹,Singh Jayant K.¹²^ORCID

Affiliation:

1. Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur-208016, India

2. Prescience Insilico Private Limited, Bangalore 560049, India

Abstract

We establish a robust and broadly applicable multistep workflow using machine learning algorithms to construct well-trained data-driven models for predicting the hydrogen evolution reaction activity of 4500 MM′XT2-type MXenes.

Funder

Science and Engineering Research Board

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D3TA00344B

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5. Two-Dimensional Hybrid Nanosheets of Tungsten Disulfide and Reduced Graphene Oxide as Catalysts for Enhanced Hydrogen Evolution

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