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.

这项职业项目将项目将Promputer模拟和机器学习算法，以提高对多孔材料中气体的基本了解。取决于吸附，包括药物储存和储能，收获水以及人类的整体社会福祉。包括医疗保健和稀缺性的水平。西班牙裔社区改善了这一领域。该计划计划通过指定吸收性材料来创建吸附模型Cortant Engineering工具，消除了当前的Bottreneck姿势F筛查所有潜在的材料，并在积极的学习中开发出了药物递送，动力生产和能源（例如，氢）大气水的收获和碳caper虫技术。）框架的吸附物和吸附物的化学空间将表示为“最大程度地融合的特征对特征空间的探索探索。气体吸附的材料。西班牙西班牙裔社区和波多黎各的材料。该奖项反映了NSF'Sf'sf'sf'Sf'Sf'Story m Ission，并被认为值得通过评估的智力优点和更广泛的影响来获得支持。