CAREER: Large Scale Simulations Enabled Materials Engineering for Heterogeneous Ice Nucleation

PI：Sarupria，Sapna提案编号：1653352机构：克莱姆森大学标题：Career：大规模模拟支持的非均质成核材料工程了解和控制冰核是一个重要的问题，在食品、交通和能源发电行业有许多潜在的应用。这项提议的目标是开发一种结合分子模拟和数据分析的综合方法，以实现对调节冰核形成的材料的高通量筛选。该项目将专注于两个系统，一个涉及自组装单分子层上的冰核，另一个是冰核蛋白质。所提出的方法是基于这样一种假设，即在界面水性质中存在材料成冰倾向的特征特征。该项目旨在识别这些特征，并制定加速冰核形成的分子描述符。拟议的教育计划的目标是使用综合方法来提高研究生和本科生对计算材料科学多学科领域的理解。拟议的活动包括在计算材料科学模拟挑战的背景下制定的黑客马拉松，以及旨在视觉学习环境中构建纳米材料的MolLego触摸屏游戏(类似于Lego)。拟议的研究计划旨在了解自组装单分子层和成冰蛋白质附近的成冰热力学和动力学。与传统的分子动力学模拟相比，皮？S团队开发的计算工具将成核事件的采样速度提高了20倍以上，从而使近表面成冰的研究成为可能。该计划将分析成核事件、自由能景观和界面水性质的大量数据集，以确定表明表面成冰倾向的关键特征。基于对关键特征的了解，将使用基于膜的表面对表面成核倾向进行高通量筛选。这项拟议的研究可能会带来计算效率高、成本效益高的技术，用于筛选材料的冰核倾向。

PI: Sarupria, Sapna Proposal Number: 1653352 Institution: Clemson University Title: CAREER: Large Scale Simulations Enabled Materials Engineering for Heterogeneous Ice Nucleation 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.