权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Navigating Thermodynamic Landscapes for Phase Equilibria Predictions using Molecular Modeling and Machine Learning

职业：利用分子建模和机器学习在热力学景观中进行相平衡预测

基本信息

批准号：
2143346
负责人：
Yamil Colon
金额：
$ 51.08万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2027-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143346&HistoricalAwards=false
关键词：
CAREER Navigating Thermodynamic Landscapes Phase

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This CAREER project will use advanced computer simulations and machine learning algorithms to advance fundamental understanding of adsorption of gases in porous materials. Adsorption involves the concentration or rejection of molecules interacting with a material surface. It is a ubiquitous phenomenon present in our everyday lives and in many industrial and biological settings. Important technological applications that depend on adsorption processes include drug delivery, power production and energy storage, water harvesting, and others that affect the overall societal well-being of humanity. This research project makes use of powerful computational modeling tools to uncover a comprehensive picture of the interactions between the gas species and materials onto which they adsorb. This research will lead to fundamental insights into the adsorption process and the identification of promising new adsorbents that are crucial for technological advancements in areas of national importance including health care, climate change, and water scarcity. Integrated outreach and education components within this project include increasing literacy of machine learning at the undergraduate and graduate levels through course design; hosting middle school teachers through the Notre Dame Senior STEM Teaching Fellows Residency program to create course materials for 6-8th graders centered on probability and statistics; and translation of the middle school course material into Spanish for dissemination to Hispanic communities to improve their representation in STEM fields.This research program will integrate advanced molecular modeling and machine learning methods to create a universal gas adsorption model. By specifying the absorbent material, an adsorbate gas species, and the adsorption conditions (temperature and pressure), the model will be able to accurately predict the amount of gas that is adsorbed within the material pores at equilibrium. An adsorption model with such predictive capabilities would constitute an important engineering design tool, eliminating the current bottleneck posed by the high computational cost of screening all potential materials with molecular simulations and fundamentally advancing drug delivery, power production and energy storage (e.g., hydrogen), and atmospheric water harvesting and carbon capture technologies. The development of models to predict the nature of gas physisorption in porous materials will be developed within an active learning (AL) framework to efficiently navigate the large chemical spaces of adsorbates and adsorbents. The properties of absorbent materials and gas molecules will be represented as ‘features’ alchemically to maximize the range of materials and molecules that can be studied in a computationally feasible manner. The AL algorithm will inform, in an automated fashion, which simulations to perform to achieve accurate predictions with a limited number of simulations, thus allowing for an exhaustive yet efficient exploration of the feature space. The research plan is based on three objectives: (1) implement and validate an active learning framework capable of navigating adsorption landscapes, (2) navigate the feature landscapes of simple gas adsorbates, and (3) simultaneously navigate the feature landscapes of molecules and porous materials for gas adsorption. Because the proposed AL framework will be readily adaptable to other adsorption/material design scenarios, phase equilibrium studies beyond gas adsorption will benefit. These research efforts will be complemented by outreach efforts to middle schools and the public through bilingual curriculum development and middle school teacher training in probability and statistics, and dissemination of the course materials in Spanish to the local Hispanic community and in Puerto Rico.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。该职业项目将使用先进的计算机模拟和机器学习算法来增进对多孔材料中气体吸附的基本了解。吸附涉及与材料表面相互作用的分子的浓缩或排斥。这是我们日常生活以及许多工业和生物环境中普遍存在的现象。依赖于吸附过程的重要技术应用包括药物输送、发电和能量储存、集水以及影响人类整体社会福祉的其他技术应用。该研究项目利用强大的计算建模工具来全面揭示气体种类与其吸附材料之间的相互作用。这项研究将带来对吸附过程的基本见解，并确定有前途的新型吸附剂，这些吸附剂对于医疗保健、气候变化和水资源短缺等国家重要领域的技术进步至关重要。该项目中的综合外展和教育部分包括通过课程设计提高本科生和研究生的机器学习素养；通过圣母大学高级 STEM 教学研究员驻场计划接待中学教师，为 6-8 年级学生制作以概率和统计为中心的课程材料；将中学课程材料翻译成西班牙语，以便向西班牙裔社区传播，以提高他们在 STEM 领域的代表性。该研究项目将整合先进的分子建模和机器学习方法，创建通用的气体吸附模型。通过指定吸收材料、吸附气体种类和吸附条件（温度和压力），该模型将能够准确预测平衡时材料孔隙内吸附的气体量。具有这种预测能力的吸附模型将构成一个重要的工程设计工具，消除当前通过分子模拟筛选所有潜在材料的高计算成本所带来的瓶颈，并从根本上推进药物输送、电力生产和能量存储（例如氢气）以及大气水收集和碳捕获技术。预测多孔材料中气体物理吸附性质的模型的开发将在主动学习（AL）框架内开发，以有效地导航吸附物和吸附剂的大型化学空间。吸收材料和气体分子的特性将以炼金术的方式表示为“特征”，以最大限度地扩大可以通过计算可行的方式研究的材料和分子的范围。 AL 算法将以自动化的方式告知要执行哪些模拟，以通过有限数量的模拟实现准确的预测，从而允许对特征空间进行详尽而有效的探索。该研究计划基于三个目标：（1）实施和验证能够导航吸附景观的主动学习框架，（2）导航简单气体吸附物的特征景观，以及（3）同时导航气体吸附分子和多孔材料的特征景观。由于所提出的 AL 框架将很容易适应其他吸附/材料设计方案，因此气体吸附以外的相平衡研究将受益。这些研究工作将通过双语课程开发和中学教师概率与统计培训以及向当地西班牙裔社区和波多黎各传播西班牙语课程材料等方式向中学和公众进行推广工作来补充。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。