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

CDS

基本信息

批准号：
1953245
负责人：
Srinivas Rangarajan
金额：
$ 42万
依托单位：
Lehigh University
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=1953245&HistoricalAwards=false
关键词：
CDS&amp Collaborative Research Towards computational

项目摘要

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有机材料类别，其应用包括分子分离，催化，储能，储能，半导体，药物输送和单分子传感器。它们是通过有机建筑块（BB）分子的动态共价组装来构建的，该过程中BBS本质上是聚合形成扩展晶体材料的过程。 COF“ Universe”，即所有可能的COF结构的集合，是巨大的，可能是数十亿个潜在的候选者，具有不同的孔径和几何形状，化学成分和功能。到目前为止，该“宇宙”只有一小部分已经合成，尽管不断报告新的COF。然而，仍然很少有两种以上毛孔的COF，这是一个关键的缺点，因为具有孔径组合的COF在约束，分子分离过程和气体储存应用下增强催化有望增强催化。设计合成的COF（称为网状化学）的一个重要想法是鉴定可以通过共价键将其独特地组装到所需的COF模式中的刚性BB分子。尽管非常有用，但相关的化学设计通常是手动实施的，因此无法扩展到更复杂的拓扑结构，也不可能枚举庞大的COF空间。在这种情况下，这项研究的目的是开发一种计算方法，以自动生成具有多个孔径的合成可行的2D共价有机框架，从而指导实验性努力以综合复杂的结构。合成可行性考虑了是否可以使用已知的化学和起始材料轻松地创建构件，并很容易组装到必要的晶体COF结构中。这项研究将化学信息技术，反应网络产生，先进的分子模拟和人工智能的工具汇集在一起，以创建一种自动化的方法来识别可用的cof，并可以使用有机化的材料来建立自动化的方法。该方法将用于创建具有复杂（特别是异孔）拓扑的COF数据库。在给定目标结构的情况下，该方法将通过粗粒分子样片状粒子模拟来识别必要的构件结构及其化学功能。最终的信息将用于使用基于增强学习的偏置自动化网络生成过程来生成潜在的分子构建块。然后，将使用化学形式工具和算法评估这些分子构建块的合成复杂性。对于最可行的分子，将使用自动化网络生成的缩回合成概念生成它们的合成途径。此过程的最终结果将是理论上确定的合成可行COF，其构建块和它们的合成路线的列表。此类列表将针对每个平铺编译，并根据综合分数进行排名。通过此策略确定的一些最有希望的COF将在涉及合成构件块及其组装的自下而上的组装中进行实验验证，并使用正交反应化学构成。为了整合研究和教育，PI将采用学生主导的原始科学研究内容的概念作为现代培训的一部分或“班级采购”，通过设计课程项目，其中整个学生的累积专业知识可利用整个学生的累积专业知识，以确定策略，以确定综合新的构件，从而通过网络授予的规则来改善nsf的规则。优点和更广泛的影响审查标准。