Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction...

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Bibliographic Details
Published in:	Chemical science (Cambridge) Vol. 1; no. 27; pp. 6697 - 676
Main Authors:	Amar, Yehia, Schweidtmann, Artur M, Deutsch, Paul, Cao, Liwei, Lapkin, Alexei
Format:	Journal Article
Language:	English
Published:	England Royal Society of Chemistry 21.07.2019
Subjects:	Amides Artificial intelligence Bayesian analysis Catalysis Charge density Chemistry Conversion Correlation coefficients Design of experiments Gaussian process Genetic algorithms Machine learning Multiple objective analysis Optimization Solvents Workflow
ISSN:	2041-6520, 2041-6539
Online Access:	Get full text
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