Title: Combining molecular dynamics simulations and machine learning to quantify interfacial hydrophobicity

Abstract: Classical molecular dynamics (MD) simulations generate high-dimensional datasets consisting of 10^3-10^6 atomic positions at 10^5-10^7 distinct timesteps. These datasets encode spatial and temporal correlations between molecules that are physically meaningful when analyzed using statistical mechanics, but such approaches are time-consuming and currently require significant human intervention. In this talk, I will describe machine learning (ML) approaches to efficiently analyze the output of MD simulations. As a representative case study, I will focus on understanding the hydrophobicity of functionalized interfaces. Hydrophobicity is a key property that influences behavior in aqueous environments but is challenging to predict for chemically heterogeneous interfaces – i.e., interfaces that have nonpolar and polar groups in close (~nm) proximity – which are abundant in biological and industrial systems. I will show how ML can extract dominant features from MD-derived solvent configurations to quantify hydrophobicity using significantly less MD data than conventional simulations. I will also demonstrate how the transformation of MD output into various data representations influences the selection of ML method and corresponding prediction accuracy. Finally, I will show that the topological analysis of MD output can outperform more complex ML models, highlighting future opportunities for the intersection of topological data analysis with MD.

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