Journal article
Will they co-crystallize?
- Abstract:
- LawrencebA data-driven approach to predicting co-crystal formation reduces the number of experiments required to successfully produce new co-crystals. A machine learning algorithm trained on an in-house set of co-crystallization experiments results in a 2.6-fold enrichment of successful co-crystal formation in a ranked list of co-formers, using an unseen set of paracetamol test experiments.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Authors
Funding
University College Cork 2013 Research Fund
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Bibliographic Details
- Publisher:
- Royal Society of Chemistry Publisher's website
- Journal:
- CrystEngComm Journal website
- Volume:
- 19
- Pages:
- 5336-5340
- Publication date:
- 2017-07-01
- Acceptance date:
- 2017-07-05
- DOI:
Item Description
- Pubs id:
-
pubs:703047
- UUID:
-
uuid:24e0e4a1-4d27-4878-a2cb-ea511bacc185
- Local pid:
- pubs:703047
- Source identifiers:
-
703047
- Deposit date:
- 2017-07-05
Terms of use
- Copyright holder:
- Royal Society of Chemistry
- Copyright date:
- 2017
- Notes:
- © The Royal Society of Chemistry 2017. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
- Licence:
- CC Attribution (CC BY)
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