Upgrading the Density Functional Theory with Machine Learning for the Fast Prediction of Polyaromatic Reactivity at Bimetallic Catalysts-Reference-Cited by-同舟云学术

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Upgrading the Density Functional Theory with Machine Learning for the Fast Prediction of Polyaromatic Reactivity at Bimetallic Catalysts

Published:2024-03-13 Issue:12 Volume:128 Page:5084-5092
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Zaffran Jérémie¹^ORCID,Jiao Minyang²,Wischert Raphaël¹,Streiff Stéphane¹,Paul Sébastien²

Affiliation:

1. Eco-Efficient Products and Processes Laboratory (E2P2L), UMI 3464 CNRS-Solvay, 3966 Jin Du Road, Xin Zhuang Ind. Zone, 201108 Shanghai, China

2. Univ. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181 - UCCS - Unité de Catalyse et Chimie du Solide, F-59000 Lille, France

Funder

Universit? de Lille

Solvay

M?tropole Europ?enne de Lille

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c00461

Reference46 articles.

1. Polyaromatics

2. Organic Compounds Containing Aromatic Structure Used in Pharmaceutical Production

3. Density functional theory in the solid state

4. Density functional theory in surface chemistry and catalysis

5. Towards the computational design of solid catalysts

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