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

职业：大规模模拟支持异质冰核材料工程

基本信息

批准号：
2224643
负责人：
Sapna Sarupria
金额：
$ 50.38万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224643&HistoricalAwards=false
关键词：
CAREER Large Scale Simulations Enabled

项目摘要

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.

PI：Sarupria，Sapna提案编号：1653352机构：克莱姆森大学职业：职业：实现大规模模拟的材料工程，用于理解和控制冰核是一个重要问题，在食品，运输和能源产业中有许多潜在的应用。该提案的目的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。该项目将集中在两个系统上，一个系统涉及自组装单层上的冰核和第二个冰核蛋白。提出的方法基于以下假设：界面水性质内有材料冰核倾向的特征特征。该项目旨在确定这些特征并制定加速冰核的分子描述。拟议的教育计划的目的是使用综合方法来通过研究生和本科生来提高计算材料科学的多学科领域的理解。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）。拟议的研究计划旨在了解靠近自组成的单核蛋白质蛋白质蛋白质的冰核的热力学和动力学。与传统的分子动力学模拟相比，PI组中开发的计算工具加快了成核事件的抽样速度超过20倍，从而实现了对表面附近冰成核的研究。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，以识别指示表面冰核倾向的关键特性。基于对钥匙特征的知识，将使用基于膜的表面进行表面成核倾向的高通量筛选。拟议的研究将有可能导致用于筛选冰核倾向的材料的计算有效的成本效益技术。