Speaker: Jules Schleinitz
Time: 2pm – 3:30pm 5th Sep (Monday)
Location: online (Zoom meeting link will be available one week before the event)
Title: Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings
Abstract
Synthetic yield prediction using machine learning is intensively studied. Previous work has focused on two categories of data sets: high-throughput experimentation data, as an ideal case study, and data sets extracted from proprietary databases, which are known to have a strong reporting bias toward high yields. However, predicting yields using published reaction data remains elusive. To fill the gap, we built a data set on nickel-catalyzed cross-couplings extracted from organic reaction publications, including scope and optimization information. We demonstrate the importance of including optimization data as a source of failed experiments and emphasize how publication constraints shape the exploration of the chemical space by the synthetic community. While machine learning models still fail to perform out-of-sample predictions, this work shows that adding chemical knowledge enables fair predictions in a low-data regime. Eventually, we hope that this unique public database will foster further improvements of machine learning methods for reaction yield prediction in a more realistic context.
About the speaker:
Jules Schleinitz completed a bachelor in chemistry and physics and then a master in theoretical chemistry at the École Normale Supérieure in Paris, then was recruited for a three year PhD at École Normale Supérieure under a teaching contract. He will defend his Ph.D thesis entitled "Mechanistic Analysis and Machine Learning" in October. In November he will start a postdoc for Computer Assisted Synthesis in Sarah E. Reisman's group at Caltech.
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