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

PI: Sarupria， Sapna提案号：1653352机构：克莱姆森大学头衔：职业：大规模模拟非均质冰成核的使能材料工程理解和控制冰成核是一个重要的问题，在食品，运输和能源生产行业有许多潜在的应用。本提案的目标是开发一种结合分子模拟和数据分析的综合方法，以实现调节冰核的材料的高通量筛选。该项目将重点关注两个系统，一个涉及自组装单层的冰核，另一个涉及冰核蛋白质。提出的方法是基于一个假设，即在界面水性质中存在材料的冰成核倾向的特征特征。该项目旨在识别这些特征，并制定加速冰核的分子描述符。提出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。提议的活动包括在计算材料科学模拟挑战背景下制定的黑客马拉松，以及旨在在视觉学习环境中构建纳米级材料的molLego触摸屏游戏（类似于Lego）。提出的研究计划旨在了解自组装单层和冰核蛋白附近冰核的热力学和动力学。PI开发的计算工具？与传统的分子动力学模拟相比，该小组将成核事件的采样速度提高了20倍以上，从而使研究近表面冰的成核成为可能。该计划是分析成核事件、自由能景观和界面水性质的广泛数据集，以确定表明表面冰成核倾向的关键特征。基于关键特征的知识，表面成核倾向的高通量筛选将使用膜基表面进行。提出的研究将有可能导致计算效率高，成本效益高的筛选材料的冰成核倾向的技术。

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.