Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
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Rational solvent selection remains a significant challenge in process development.
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- Authors
- Amar, Y., Schweidtmann, A. M., Deutsch, P., Cao, L., & Lapkin, A.
- Affiliations
- University of Cambridge (GB)
- RWTH Aachen University (DE)
- UCB Pharma S.A. Allée de la Recherche
- Corresponding author
- Lapkin, Alexei
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2019
- Volume
- 10
- Issue
- 27
- Pages
- 6697-6706
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC6625492
- OpenAlex
- W2946850705
- Cited by
- 132
- References
- 57
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 18 · 2023: 20 · 2022: 23 · 2021: 25 · 2020: 12 · 2019: 5
- FWCI
- 5.87
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Chemical Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- RWTH Publications (RWTH Aachen)
- Funders
- UCB
- BASF
- National Research Foundation Singapore
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Inorganic Chemistry › Asymmetric Hydrogenation and Catalysis
- Physical Sciences › Chemistry › Organic Chemistry › Asymmetric Synthesis and Catalysis
- Keywords
- solvent selection, asymmetric catalysis, asymmetric hydrogenation, multi-objective optimization, diastereomeric excess, Kriging surrogate model, Bayesian optimization, genetic programming, molecular descriptors, robotics laboratory, experimental design
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