Understanding and controlling ice nucleation is an important problem with numerous potential applications in the food, transportation, and energy generation industries. The goal of this proposal is to develop an integrated approach combining molecular simulations and data analysis to enable the high-throughput screening of materials that modulate ice nucleation. The project will focus on two systems, one involving ice nucleation on self-assembled monolayers, and the second ice-nucleating proteins. The proposed approach is based on the hypothesis that there are characteristic signatures of a material's ice nucleating propensity within the interfacial water properties. The project aims to identify those signatures and formulate molecular descriptors that accelerate ice nucleation. The goal of the proposed education plan is to use an integrated approach to improve the understanding of the multidisciplinary field of computational materials science by graduate and undergraduate students. The proposed activities include a hackathon formulated in the context of computational materials science simulation challenges and a molLego touch-screen game (similar to Lego) aimed at constructing nanoscale materials in a visual learning environment.
The proposed research plan aims to understand the thermodynamics and kinetics of ice nucleation near self-assembled monolayers and ice-nucleating proteins. Computational tools developed in the PI?s group speed up sampling of nucleation events by more than a factor of 20 compared to traditional Molecular Dynamics simulations, thereby enabling studies of ice nucleation near surfaces. The plan is to analyze the extensive dataset of nucleation events, free energy landscapes, and interfacial water properties to identify the key signatures that indicate the ice-nucleating propensity of a surface. Based on knowledge of the key signatures, high-throughput screening of the surface nucleating propensity will be performed using membrane-based surfaces. The proposed research will potentially lead to computationally efficient cost-effective techniques for screening materials for ice nucleation propensity.
