Machine Learning-Driven Discovery of Key Descriptors for CO<sub>2</sub>Activation over Two-Dimensional Transition Metal Carbides and Nitrides-Reference-Cited by-同舟云学术

Machine Learning-Driven Discovery of Key Descriptors for CO₂Activation over Two-Dimensional Transition Metal Carbides and Nitrides

Published:2023-06-19 Issue:25 Volume:15 Page:30117-30126
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Abraham B. Moses¹²^ORCID,Piqué Oriol²^ORCID,Khan Mohd Aamir¹³,Viñes Francesc²^ORCID,Illas Francesc²^ORCID,Singh Jayant K.¹³^ORCID

Affiliation:

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

2. Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1-11, Barcelona 08028, Spain

3. Prescience Insilico Private Limited, Bangalore 560049, India

Funder

European Commission

Science and Engineering Research Board

Agencia Estatal de Investigaci?n

European Cooperation in Science and Technology

Horizon 2020 Framework Programme

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.3c02821

Reference70 articles.

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2. Nanostructured nonprecious metal catalysts for electrochemical reduction of carbon dioxide

3. Towards a better Sn: Efficient electrocatalytic reduction of CO 2 to formate by Sn/SnS 2 derived from SnS 2 nanosheets

4. Recent Progress in the Theoretical Investigation of Electrocatalytic Reduction of CO2

5. Size-Dependent Electrocatalytic Reduction of CO2 over Pd Nanoparticles

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