Collaborative neural networks-accelerated prediction of transition state energy barriers for CO catalytic oxidation-Reference-Cited by-同舟云学术

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Collaborative neural networks-accelerated prediction of transition state energy barriers for CO catalytic oxidation

Published:2024-10 Issue: Volume:1002 Page:175546
ISSN:0925-8388
Container-title:Journal of Alloys and Compounds
language:en
Short-container-title:Journal of Alloys and Compounds

Author:

Tang Tian,Xue Jingyu,Shen Xiaoqiang,Chen Jinfei,Rac Vladislav,Rakić Vesna,Li Xinbao,Zhou Bo,Chen Yanrong,Song Hao,Du Xuesen

Funder

Natural Science Foundation of Chongqing Municipality

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference47 articles.

1. Acid and basic catalysis;Bronsted;Chem. Rev.,1928

2. Inertia and driving force of chemical reactions;Evans;Trans. Faraday Soc.,1938

3. The Brønsted–Evans–Polanyi relation and the volcano plot for ammonia synthesis over transition metal catalysts;Logadottir;J. Catal.,2001

4. Predicting chemical reaction barriers with a machine learning model;Singh;Catal. Lett.,2019

5. MXene-based single atom catalysts for efficient CO2RR towards CO: A novel strategy for high-throughput catalyst design and screening;Cao;Chem. Eng. J.,2023

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