Nitrogen reduction reaction energy and pathways in metal-zeolites: deep learning and explainable machine learning with local acidity and hydrogen bonding features-Reference-Cited by-同舟云学术

Nitrogen reduction reaction energy and pathways in metal-zeolites: deep learning and explainable machine learning with local acidity and hydrogen bonding features

Published:2022 Issue:28 Volume:10 Page:14976-14988
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Gu Yuming¹,Zhu Qin¹,Liu Ziteng¹,Fu Cheng¹,Wu Jiayue¹^ORCID,Zhu Qiang¹^ORCID,Jia Qingqing¹,Ma Jing¹^ORCID

Affiliation:

1. Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China

Abstract

The synergistic effect between metal coordination and HB interactions could realize nitrogen fixation in metal-zeolites, which was studied by deep learning and feature learning.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

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/2022/TA/D2TA03563D

Reference73 articles.

1. Challenges in reduction of dinitrogen by proton and electron transfer

2. Theoretical screening of single atoms anchored on defective graphene for electrocatalytic N2 reduction reactions: a DFT study