Abstract
A 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.
| Original language | English |
|---|---|
| Pages (from-to) | 5336-5340 |
| Number of pages | 5 |
| Journal | CrystEngComm |
| Volume | 19 |
| Issue number | 36 |
| DOIs | |
| Publication status | Published - 2017 |
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