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

主要研究者：Sarupria，Sapna提案编号：1653352机构：克莱姆森大学标题：职业生涯：大规模模拟启用材料工程异质冰成核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的许多潜在应用。该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，以实现对调节冰成核的材料的高通量筛选。该项目将集中在两个系统，一个涉及自组装单层上的冰成核，第二个涉及冰成核蛋白质。所提出的方法是基于这样的假设，即有一种材料的冰成核倾向的特征签名内的界面水的属性。该项目旨在识别这些特征并制定加速冰成核的分子描述符。拟议的教育计划的目标是使用一种综合的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和molLego触摸屏游戏（类似于乐高）的背景下制定的黑客游戏，旨在构建纳米级材料的视觉学习环境。拟议的研究计划旨在了解自组装单分子膜和冰成核蛋白附近的冰成核热力学和动力学。在PI中开发的计算工具？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，从而能够研究表面附近的冰成核。该计划将分析大量的成核事件、自由能景观和界面水特性数据集，以确定表明表面冰成核倾向的关键特征。基于对关键特征的了解，将使用膜基表面进行表面成核倾向的高通量筛选。拟议的研究将可能导致计算效率高，成本效益高的技术筛选材料的冰成核倾向。

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.