Collaborative Research: Computational Design of Metal-Organic Framework Materials

合作研究：金属有机框架材料的计算设计

• 批准号: 1663360
• 负责人: Peter Greaney
• 金额: $ 26.92万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663360&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Design; Metal

This award supports research in computational methods for the automatic exploration of the search space of metal-organic frameworks to obtain desired combinations of mechanical, thermal and chemical properties. A metal-organic framework is a repeating three-dimensional crystal lattice with a large open space-frame structure composed of organic linking molecules bonded to inorganic nodal units. The interconnected pore-spaces in these materials endow them with a rich variety of unusual properties that can be exploited for gas storage, gas separation, and catalysis. Given the constraints of chemical bonds and bond angles, one cannot easily customize the lattice for a particular size and shape. Thankfully there is an astronomically large number of permutations for combining various atomic elements together. However, human designers are confounded on how to find one for their particular problem domain. What is needed is a computational process to search the space for a best solution. By formalizing the molecular design as a decision tree, the developed computer algorithms will invent new materials whose functional behavior is defined by its chemical makeup and the resulting geometry and movement of the lattice structure. As part of testing the design approach, the project will address two technologically important problems: designing new metal organic frameworks optimized for gas storage, and materials for separating isomers in industrially important chemical feed stocks. STEM outreach activities to high school students and teachers will also be performed.Specifically, the project seeks materials that exhibit highly chemically selective adsorption or permeability of gases through mechanisms that arise from chemical, steric, and vibrational behavior of the frameworks. The computational search for this incorporates a unique graph transformation approach that mimics correct stoichiometric reactions and leads to a large search tree that is amenable to recent advances in artificial intelligence planning algorithms. Furthermore, machine learning methods will establish a link between the structure and function of organic frameworks by leveraging data from complex molecular simulations. This will lead to more efficient search of the decision tree so that meaningful results can be obtained. Through detailed simulations of the resulting metal organic frameworks, the researchers will publish how their new materials can be used to tackle challenging problems in energy storage, high-tech manufacturing, and the creation of new sensitive sensor equipment.

该奖项支持计算方法的研究，用于自动探索金属有机框架的搜索空间，以获得所需的机械，热和化学性质的组合。金属-有机骨架是由有机连接分子与无机节点单元键合而成的具有大的开放空间框架结构的重复三维晶格。这些材料中相互连通的孔隙空间赋予它们丰富多样的不寻常特性，可用于气体储存，气体分离和催化。考虑到化学键和键角的限制，人们不能容易地定制特定尺寸和形状的晶格。幸运的是，有大量的排列组合各种原子元素在一起。然而，人类设计师对于如何为他们的特定问题域找到一个问题域感到困惑。所需要的是一个计算过程，以搜索空间的最佳解决方案。通过将分子设计形式化为决策树，开发的计算机算法将发明新材料，其功能行为由其化学组成和由此产生的晶格结构的几何形状和运动定义。作为测试设计方法的一部分，该项目将解决两个技术上重要的问题：设计新的金属有机框架，优化气体储存，以及分离工业上重要的化学原料中的异构体的材料。此外，还将开展面向高中生和教师的STEM推广活动。具体而言，该项目寻求通过框架的化学、空间和振动行为产生的机制，表现出高度化学选择性吸附或气体渗透性的材料。计算搜索，这结合了一个独特的图形转换方法，模仿正确的化学计量反应，并导致一个大的搜索树，是服从人工智能规划算法的最新进展。此外，机器学习方法将通过利用来自复杂分子模拟的数据，在有机框架的结构和功能之间建立联系。这将导致决策树的更有效的搜索，从而可以获得有意义的结果。通过对所得金属有机框架的详细模拟，研究人员将公布他们的新材料如何用于解决能源储存，高科技制造和新型敏感传感器设备的创造中的挑战性问题。

项目成果

Metal‐Mediated Directional Capping of Rod‐Packing Metal–Organic Frameworks

金属-介导的棒定向封端-填充金属-有机框架

• DOI: 10.1002/chem.202201576
• 发表时间: 2022
• 期刊: Chemistry – A European Journal
• 作者: Hong, Anh N.;Luong, Diana;Alghamdi, Mohammed;Liao, Wei‐Cheng;Zhang, Weiyi;Kusumoputro, Emily;Chen, Yichong;Greaney, P. Alex;Cui, Yongtao;Shi, Jing
• 通讯作者: Shi, Jing