Ab initio derived group additivity model for intramolecular hydrogen abstraction reactions-Reference-Cited by-同舟云学术

Ab initio derived group additivity model for intramolecular hydrogen abstraction reactions

Published:2018 Issue:16 Volume:20 Page:10877-10894
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Van de Vijver Ruben¹²³⁴^ORCID,Sabbe Maarten K.¹²³⁴^ORCID,Reyniers Marie-Françoise¹²³⁴^ORCID,Van Geem Kevin M.¹²³⁴^ORCID,Marin Guy B.¹²³⁴

Affiliation:

1. Ghent University

2. Laboratory for Chemical Technology

3. Ghent

4. Belgium

Abstract

A systematic methodology to calculate reaction rate coefficients of intramolecular hydrogen abstraction reactions by group additivity has been developed.

Funder

European Research Council

Agentschap voor Innovatie door Wetenschap en Technologie

Seventh Framework Programme

Universiteit Gent

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2018/CP/C7CP07771H

Reference55 articles.

1. Additivity Rules for the Estimation of Molecular Properties. Thermodynamic Properties

2. Automatic Mechanism and Kinetic Model Generation for Gas- and Solution-Phase Processes: A Perspective on Best Practices, Recent Advances, and Future Challenges

3. Ab Initio Calculations for Hydrocarbons: Enthalpy of Formation, Transition State Geometry, and Activation Energy for Radical Reactions

4. Ab initio group contribution method for activation energies for radical additions

5. Carbon-Centered Radical Addition and β-Scission Reactions: Modeling of Activation Energies and Pre-exponential Factors

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates;Chemical Science;2024

2. A machine learning based approach to reaction rate estimation;Reaction Chemistry & Engineering;2024

3. Chemical bonding in potential PFAS products from the thermal degradation of energetic devices, a DFT analysis;Chemosphere;2023-12

4. Beyond group additivity: Transfer learning for molecular thermochemistry prediction;Chemical Engineering Journal;2023-09

5. Steam cracking of methyl ester: A modeling study on the influence of the hydrocarbon backbone;Journal of Analytical and Applied Pyrolysis;2023-06