Speaker: Dr. Jules SCHLEINITZ

Institution: École Normale Supérieure PSL, Paris

Hosted By: Professor Haibin SU

Co-Host: Professor Zhenyang LIN

Zoom Link: https://hkust.zoom.us/j/95150435343?pwd=Y2g0L3BpRmZWeStVZGVjOHUxalBFUT09

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.