CAS: Computational Data-Driven Metal-Free Catalysts Discovery for Small Molecule Activation and Conversion

CAS：计算数据驱动的无金属催化剂发现，用于小分子活化和转化

• 批准号: 2247481
• 负责人: Jingyun Ye
• 金额: $ 42.44万
• 依托单位: Duquesne University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247481&HistoricalAwards=false
• 关键词: CAS; Computational; Data; Driven; Metal

In this project, funded by the Chemical Structure, Dynamics & Mechanisms-B Program of the Chemistry Division, Jingyun Ye of the Department of Chemistry and Biochemistry at Duquesne University aims to design and discover energy-saving, environmentally friendly, and low-cost metal-free catalysts for small molecule activation. Small molecule conversions such as the conversion of carbon dioxide to useful chemicals and fuels have the potential to reduce reliance on fossil carbon sources and build a more renewable carbon cycle. The approach taken here is based on frustrated Lewis pairs (FLPs). This project aims to construct an open access FLP database and to combine quantum mechanical modeling with data science and machine learning to accelerate the discovery of novel FLPs with targeted catalytic reactivity for small molecule activation and conversion. The proposed research will have significant educational and research opportunities for the next generation of researchers in the cross-disciplinary fields of chemistry, computational modeling, materials science, catalysis, data science, and artificial intelligence, with a particular focus on promoting the participation of members of underrepresented groups and women in science. Frustrated Lewis pairs are the simple combination of a bulky Lewis acid and a bulky Lewis base that are sterically precluded from reacting with each other. The unquenched Lewis acid and Lewis base sites are available to accept and donate electron density, respectively, providing a unique route to the activation and catalytic conversion of small molecules. The overall research goal of this project is to reveal the structure-activity relationships of FLPs for small molecule activation and the design of novel and efficient metal-free catalysts for energy conversion and environmental concerns via the combination of density functional theory, data science, and machine learning. The project objectives are to: (1) construct an open-access FLPs database that contains structural, electronic, and energetic data of FLPs and their activity toward small molecule activation; (2) identify the structure-activity relationships of FLPs for carbon dioxide hydrogenation and alkyne semi-hydrogenation using machine learning; and (3) design FLPs with targeted properties for small molecule activation and catalysis via deep learning.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.

在该项目中，由化学部化学结构，动力学机制-B计划资助，迪克讷大学化学与生物化学系的Jingyun Ye旨在设计和发现节能，环保，低成本的无金属催化剂用于小分子活化。 小分子转化，如将二氧化碳转化为有用的化学品和燃料，有可能减少对化石碳源的依赖，并建立一个更可再生的碳循环。 这里采用的方法是基于受挫的刘易斯对（FLP）。 该项目旨在构建一个开放访问的FLP数据库，并将联合收割机量子力学建模与数据科学和机器学习相结合，以加速发现具有小分子活化和转化的靶向催化反应性的新型FLP。拟议的研究将为化学，计算建模，材料科学，催化，数据科学和人工智能等跨学科领域的下一代研究人员提供重要的教育和研究机会，特别关注促进代表性不足的群体成员和女性参与科学。阻挫刘易斯对是大体积的刘易斯酸和大体积的刘易斯碱的简单组合，它们在空间上被阻止彼此反应。未淬灭的刘易斯酸和刘易斯碱位点可分别接受和提供电子密度，为小分子的活化和催化转化提供了独特的途径。该项目的总体研究目标是揭示FLP用于小分子活化的结构-活性关系，并通过密度泛函理论，数据科学和机器学习的结合，设计用于能量转换和环境问题的新型高效无金属催化剂。该项目的目标是：（1）构建一个开放访问的FLP数据库，其中包含FLP的结构、电子和能量数据及其对小分子活化的活性;（2）使用机器学习确定FLP用于二氧化碳加氢和炔半加氢的结构-活性关系;以及（3）通过深度学习设计具有小分子活化和催化目标特性的FLP。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。