Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates-Reference-Cited by-同舟云学术

Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates

Published:2024 Issue:7 Volume:15 Page:2410-2424
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Chung Yunsie¹^ORCID,Green William H.¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

Abstract

A machine learning model, trained on a large COSMO-RS dataset, enables accurate and rapid predictions of solvation effects on reaction rates for diverse reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC05353A

Reference68 articles.

1. Detailed Kinetic Modeling for the Pyrolysis of a Jet A Surrogate

2. Detailed Reaction Mechanism for 350–400 °C Pyrolysis of an Alkane, Aromatic, and Long-Chain Alkylaromatic Mixture

3. Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

4. Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing

Cited by 3 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. Perspective on automated predictive kinetics using estimates derived from large datasets;International Journal of Chemical Kinetics;2024-06-25

2. Benchmarking Strategies of Sustainable Process Chemistry Development: Human-Based, Machine Learning, and Quantum Mechanics;Organic Process Research & Development;2024-06-07

3. Notizen aus der Chemie;Nachrichten aus der Chemie;2024-02-29