Predicting Molecular Properties with Graph Neural Networks
We introduce a machine learning framework for predicting molecular properties across chemical mixtures using graph neural networks.
"Our mission is a human-centered one ... Designing computer-robotic systems that augment our human capacity for engineering and solving (broadly) chemical problems."
Recent peer-reviewed research articles and preprints
We introduce a machine learning framework for predicting molecular properties across chemical mixtures using graph neural networks.
Location: Wallberg Building (WB 222)
200 College Street, Toronto, ON M5S 3E5, Canada
Department of Chemical Engineering & Applied Chemistry
University of Toronto
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