Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From l inear‐scaling relationships to statistical learning techniques-Reference-Cited by-同舟云学术

Dimensionality reduction of complex reaction networks in heterogeneous catalysis: From l inear‐scaling relationships to statistical learning techniques

Published:2021-05-10 Issue:6 Volume:11 Page:
ISSN:1759-0876
Container-title:WIREs Computational Molecular Science
language:en
Short-container-title:WIREs Comput Mol Sci

Author:

Pablo‐García Sergio¹,García‐Muelas Rodrigo¹^ORCID,Sabadell‐Rendón Albert¹^ORCID,López Núria¹^ORCID

Affiliation:

1. Institute of Chemical Research of Catalonia The Barcelona Institute of Science and Technology Tarragona Spain

Funder

Ministerio de Ciencia e Innovación

Publisher

Wiley

Subject

Materials Chemistry,Computational Mathematics,Physical and Theoretical Chemistry,Computer Science Applications,Biochemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1540

Reference155 articles.

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2. To address surface reaction network complexity using scaling relations machine learning and DFT calculations

3. A Theoretical and Computational Analysis of Linear Free Energy Relations for the Estimation of Activation Energies

4. Effects of correlated parameters and uncertainty in electronic-structure-based chemical kinetic modelling

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