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Abstract:

Amorphous materials form a vast, yet vastly underutilized, class of materials. Computational methods help in making some sense of properties in such materials, but for the most part, the reach of computational methods remains limited due to dramatically unfavorable scaling with simulation size. In this talk, I will explain how we use generative machine learning to extend nanoscale simulations into the mesoscale for amorphous materials, achieving linear scaling with system size. I will make the case for the physicality of the generated structures and discuss some potential applications using amorphous graphene and water as examples. To conclude the talk, I will switch to biological ‘disordered materials’ and discuss our approach to computational design of single-stranded DNA electrochemical sensors by means of machine learning, molecular dynamics simulations using ‘E2EDNA’, and, surprisingly enough, the gzip compression algorithm.

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Bio:

Lena Simine (ÎÛÎÛ²ÝÝ®ÊÓƵ) is an Assistant Professor in the Chemistry department at ÎÛÎÛ²ÝÝ®ÊÓƵ University. She earned her PhD from the Chemical Physics Theory Group at the University of Toronto in 2015 and did her postdoctoral work at Rice University. She started an independent research group at ÎÛÎÛ²ÝÝ®ÊÓƵ in 2019. Her expertise is in modeling nano- to mesoscale systems with molecular-scale disorder. She combines traditional simulation techniques with AI/ML to investigate disordered materials ranging from amorphous graphene to synthetic biological sequences.