Physical Seminar Series: Ella Rajaonson, PhD Student

Ella is a PhD student working in the Matter Lab and the Chemical Cognition Lab. Her research interests include property prediction of chemical mixtures, large scale molecular virtual screening, and generative modelling for molecular design.

Predicting the properties of molecular mixtures using Machine Learning

Original Graduate Research

Abstract: Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of the industry pipeline, the chemical mixture space remains relatively unexplored by the Machine Learning community. In this talk, I will introduce CheMixHub, a holistic benchmark for molecular mixtures, covering a corpus of 11 chemical mixtures property prediction tasks, from drug delivery formulations to battery electrolytes, totalling approximately 500k data points gathered and curated from 7 publicly available datasets. CheMixHub introduces various data splitting techniques to assess context-specific generalization and model robustness, providing a foundation for the development of predictive models for chemical mixture properties. Furthermore, we map out the modelling space of deep learning models for chemical mixtures, establishing initial benchmarks for the community. This dataset has the potential to accelerate chemical mixture development, encompassing reformulation, optimization, and discovery.

Zoom Meeting Link: https://utoronto.zoom.us/j/84889438656
Zoom Meeting ID: 848 8943 8656
Passcode: pchemrocks

CHM 1490 Students, please see Quercus for full speaker schedule.