Numerous disciplines, such as image recognition and machine translation, have been revolutionized by using machine learning (ML) to leverage big data. In organic synthesis, providing accurate chemical reactivity prediction with ML models could assist chemists with reaction prediction, optimization, and mechanistic interrogation. This talk will cover my group’s efforts on experimental data collection and the quest to expand its availability and limit its bias for data science applications; feature engineering that may extend common intuition about the underlying chemistry; model assessments in the regime of small to medium size reaction datasets; and opportunities arising from accurate model predictions and their mechanistic interpretation.

Machine learning for synthetic chemists

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.