Organic Chemistry Seminar: Professor Robert Paton, Colorado State University
Organic Chemistry Seminar: Professor Robert Paton, Colorado State University (Hosts: Matthew Kanan & Thomas Markland)
**This seminar is available for in-person attendance.**
"Quantum mechanical & Data-driven predictions of open-shell reactivity & stability"
About the Seminar
Quantum chemical models of reaction mechanism and selectivity provide a powerful tool to explain the outcome of laboratory experiments. However, since many reactions involve several steps and multiple conformers, the computational expense of QM approaches often prevent their application to predict reaction outcomes more broadly. Surrogate machine-learning models with quantum chemical accuracy at a fraction of the computational cost are set to transform the accessibility of computational predictions of reactivity and selectivity. I will discuss machine learning efforts utilizing knowledge and data from QM studies to generate surrogate models for the large-scale prediction of various atomic and molecular properties. We have developed graph neural networks to predict computational and experimental observables such as spin density, chemical shift, thermochemistry and reactivity. In this talk I discuss the performance of these models in high-throughput predictions of reactivity and selectivity of heteroaromatics and in goal-directed molecular optimization of stable organic radicals, along with strategies to improve model transferability.
About the Speaker
Robert Paton carried out doctoral studies with Prof. Jonathan Goodman at the University of Cambridge and postdoctoral work with Prof. Feliu Maseras at ICIQ and Prof. K. N. Houk at UCLA. He started his independent academic career at the University of Oxford in 2010, and moved to Colorado State University in 2018, where is a Professor of Chemistry and Head of Organic Chemistry. The Paton group develops computational approaches and Python-based tools to understand and predict organic reactivity and selectivity, as co-founders of the NSF Center for Computer-Assisted Synthesis.
Image Credit: Robert Paton