CDS&E: Collaborative Research: Towards computational discovery of synthetically feasible porous organic frameworks

CDS

• 批准号: 1953246
• 负责人: Ian Hill
• 金额: $ 3万
• 依托单位: Gustavus Adolphus College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953246&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Towards; computational

Covalent organic frameworks (COFs) belong to an emerging class of organic materials with low density, high thermal stability, and stable porosity, with applications including molecular separations, catalysis, energy storage, semiconductors, drug delivery, and single-molecule sensors. They are constructed via dynamic covalent assembly of organic building-block (BB) molecules, a process wherein the BBs essentially polymerize to form an extended crystalline material. The COF “universe,” i.e. the set of all possible COF structures, is vast and potentially comprises billions of potential candidates with varying pore size and geometry, chemical composition, and functionality. Only a small fraction of this “universe” has been synthesized so far, although new COFs are continuously being reported. Nevertheless, there are still very few reported COFs with more than two types of pores, a critical shortcoming because COFs with combinations of pore sizes hold promise for enhancing catalysis under confinement, molecular separation processes, and gas storage applications. A prominent idea for designed synthesis of COFs, known as reticular chemistry, is to identify rigid BB molecules that can be uniquely assembled into the desired COF pattern via covalent bonds. While extremely useful, the related chemical design is often implemented manually, and is therefore not scalable to more complex topologies nor is it able to enumerate the vast space of COFs. In this context, the goal of this research is to develop a computational method to automatically generate synthetically feasible 2D covalent organic frameworks with multiple pore sizes and thereby guide experimental efforts to synthesize complex structures. Synthetic feasibility considers whether the building block(s) can be easily created using known chemistries and starting materials and easily assembled into the requisite crystalline COF structure.This research brings together tools from cheminformatics, reaction network generation, advanced molecular simulations, and artificial intelligence to create an automated method to identify COFs that can be synthesized using easily available starting materials and proven organic chemistries. This method will be used to create a database of COFs with complex (in particular heteroporous) topology. Given a target structure, the method will identify the necessary building block structure and its chemical functionality via coarse-grained molecule-like patchy particle simulations. The resulting information will be used to generate potential molecular building blocks using a reinforcement learning-based biased automated network generation process. The synthetic complexity of these molecular building blocks will then be evaluated using cheminformatics tools and algorithms. For the most synthetically feasible molecules, their synthesis routes will be generated using the concept of retrosynthesis via automated network generation. The end result of this process will be a list of theoretically determined synthetically feasible COFs, their building blocks, and their synthesis routes. Such lists will be compiled for each tiling and ranked based on the synthesis scores. A few of the most promising COFs identified through this strategy will be verified experimentally in a bottom-up assembly involving synthesis of the building blocks and their assembly using orthogonal reaction chemistries. To integrate research and education, the PIs will employ the concept of student-led creation of original scientific research content as part of curricular training, or “class sourcing,” by designing course projects wherein the cumulative expertise of the entire cohort of students is leveraged to identify strategies to synthesize new building blocks and thereby improve rules for network generation.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.

共价有机框架（COF）属于具有低密度、高热稳定性和稳定孔隙率的新兴类别的有机材料，其应用包括分子分离、催化、能量存储、半导体、药物递送和单分子传感器。它们是通过有机结构单元（BB）分子的动态共价组装来构建的，这是BB基本上结晶以形成延伸的结晶材料的过程。COF“宇宙”，即所有可能的COF结构的集合，是巨大的，并且潜在地包括数十亿个具有不同孔径和几何形状、化学组成和功能的潜在候选物。到目前为止，只有一小部分这个“宇宙”已经合成，尽管新的COFs不断被报道。尽管如此，仍然有非常少的报道COF具有两种以上类型的孔，一个关键的缺点，因为COF与孔径的组合保持约束下，分子分离过程中，和气体存储应用增强催化的希望。用于COF的设计合成（称为网状化学）的突出想法是鉴定可以经由共价键独特地组装成所需COF图案的刚性BB分子。虽然非常有用，但相关的化学设计通常是手动实现的，因此不能扩展到更复杂的拓扑结构，也不能枚举COF的巨大空间。在这种情况下，本研究的目标是开发一种计算方法来自动生成具有多种孔径的合成可行的2D共价有机框架，从而指导实验工作来合成复杂的结构。合成可行性考虑的是构建单元是否可以使用已知的化学物质和起始材料容易地创建，并容易地组装成所需的晶体COF结构。这项研究汇集了来自化学信息学，反应网络生成，高级分子模拟，和人工智能来创建一种自动化方法来识别COFs，这些COFs可以使用容易获得的起始材料和经过验证的有机合成化学该方法将用于创建具有复杂（特别是异孔）拓扑结构的COF的数据库。给定一个目标结构，该方法将通过粗粒度的分子状补丁粒子模拟来识别必要的构建块结构及其化学功能。由此产生的信息将用于使用基于强化学习的有偏自动网络生成过程生成潜在的分子构建模块。然后，将使用化学信息学工具和算法来评估这些分子构建块的合成复杂性。对于合成最可行的分子，其合成路线将通过自动网络生成使用逆合成的概念来生成。该过程的最终结果将是理论上确定的合成可行的COF、它们的构件和它们的合成路线的列表。这些列表将针对每个区块进行汇编，并根据综合得分进行排名。一些最有前途的COFs确定通过这种策略将在一个自下而上的组装涉及合成的积木和它们的组装使用正交反应化学实验验证。为了整合研究和教育，PI将采用学生主导的原创科学研究内容创作的概念，作为课程培训的一部分，或“课堂采购，“通过设计课程项目，利用整个学生群体的累积专业知识来确定综合新构建模块的策略，从而改进网络生成规则。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。