Machine Learning for Synthetic Chemists

Seminar series
Organic Colloquium
Fri, Jan 10 1:00pm
MSB 3440
Speaker Dr. Abigail Doyle
Princeton University

Abstract: Vast time and material resources are expended in the discovery of chemicals (e.g., medicines or biodegradable plastics) and in their manufacture. Similar constraints govern the discovery of new chemical reactions, which gate access to these chemicals. Chemistry is a data-rich field, yet data science applications in the chemical sciences have been relatively limited. This lecture will outline my group’s recent efforts to develop machine learning (ML) tools to aid chemists in reaction condition prediction, optimization, and mechanistic understanding. These tools could enable chemists to make better data-driven decisions resulting in more discoveries with fewer time and material costs.