Providing accurate chemical reactivity prediction with ML models - Jason Wang скачать в хорошем качестве

Providing accurate chemical reactivity prediction with ML models - Jason Wang

Talk on Providing accurate chemical reactivity prediction with ML models with Jason Wang given at The UK Catalysis Hub and Centre for Rapid Online Analysis of Reactions (ROAR) virtual meeting to discuss the impact of automation and digital chemistry on catalysis. 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 the Doyle 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.