Prof. Yelena Simine, McGill University, "New Methods for Simulating Disordered Materials: From Amorphous Graphene to DNA Aptamers"

Amorphous materials form a vast, yet vastly underutilized, class of materials. Computational simulations help in making some sense of their properties, but for the most part, the reach of computational methods remains limited. In the first part of the talk, I will explain how we use generative machine learning within the framework of the Morphological Autoregressive Protocol (MAP) to extend nanoscale samples into the mesoscale and to hunt for new physics in glassy systems. Two case studies that focus on amorphous graphene and on glass-forming liquids will be used to demonstrate MAP’s utility: 1. as a sampling algorithm to obtain sufficiently large samples of a materials for property prediction, and 2. as a characterization tool, for example, to diagnose dynamic heterogeneity in fragile glasses. In the second part of the talk, I will shift gears to a different type of disordered molecules: DNA aptamers. DNA aptamers are short single-stranded DNA sequences that selectively bind molecular targets and whose conformation is strongly affected by the binding event. I will discuss the key challenges in computational characterization of DNA aptamer – small-molecule complexes and new strategies for in-silico design of DNA aptasensors.