Talk Abstract
We have used PyMC3 to formulate an explainable probabilistic model of chemical reactivity. This probabilistic model combines the intuitive concepts of high school chemistry with the computer’s ability to store and reason about large datasets. We use our model in the lab, where it guides a robot chemist towards “interesting” experiments that might lead to the discovery of new reactions.
Hessam Mehr | Twitter @hessammehr |
GitHub hessammehr |
Personal website |
Dario Caramelli | Twitter @dariocaramelli |
Personal website |
Talk
Hessam Mehr
Hessam Mehr is a research associate in the Cronin group at the University of Glasgow’s School of Chemistry. He works with an interdisciplinary group of scientists and engineers to build robots and teach them how to do chemistry. Since he joined the group in 2018, Hessam’s main focus has been the integration of probabilistic reasoning with chemical robotics and discovery.
Dario Caramelli
Dario Caramelli is a research associate in the Cronin group at the University of Glasgow. His research involves building and programming of autonomous robots for reaction discovery as well as the development of algorithms for chemical space modelling and data processing. Dario obtained a Master degree in Organic chemistry in Rome (2015) and a PhD in the Cronin group (2019).
This is a PyMCon 2020 talk
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