Megan Engel: Harnessing Machine Learning to Build Better Molecular Models

Join us for a conversation with Professor Megan Engel from the University of Calgary about what it takes to build better physical models of molecules. Her work is inspired by the challenges faced by the original oxDNA developers in creating the coarse-grained force fields, and trying to understand what shortcuts are available to modern computational physicists that might reduce the difficulty-level of parameterizing new models. The conversation then branches out into her vision for designing out-of-equilibrium molecular machines with a focus on nanoscale forces and her favorite J. R. R Tolkien lore.

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Megan Engel is an assistant professor at University of Calgary whose work focuses on harnessing the technological advances underpinning artificial intelligence to push the boundaries of physical models. She did her bachelors and masters in astrophysics at the University of Alberta before doing her PhD at Oxford where she worked extensively with the oxDNA model to understand the behavior of DNA structures under forces. After a postdoc with Michael Brenner at Harvard where she dived into optimization algorithms, she returned to Alberta to start her own group at the University of Calgary focused on non-equilibrium thermodynamics and model optimization.

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Find more information at the episode page here:
https://podcast.molpi.gs/media/engel-m-b0f052d3af8e05e2/