- author = "Yehia Amar and Artur M. Schweidtmann and Paul Deutsch and Liwei Cao and Alexei Lapkin",
- title = "Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis",
- journal = "Chemical Science",
- year = "2019",
- volume = "10",
- number = "27",
- pages = "6697--6706",
- month = jul,
- note = "Edge Article",
- keywords = "genetic algorithms, genetic programming, TPOT, gamultiobj, matlab, GP surrogate models, in silico modeling",
- publisher = "Royal Society of Chemistry",
-
URL = "
https://pubs.rsc.org/en/content/articlepdf/2019/sc/c9sc01844a",
-
DOI = "
doi:10.1039/C9SC01844A",
- size = "10 pages",
- abstract = "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-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)2(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral alpha-beta unsaturated gamma-lactam. With two simultaneous objectives: high conversion and high diastereomeric excess, the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.",
-
notes = "p6704 'the automated machine learning workflow was
successfully used for the problem of solvent selection
.. supplemented .. surrogate model'
Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
http://rsc.li/chemical-science",
