Collaborative Research: Data-Driven Microreaction Engineering by Autonomous Robotic Experimentation in Flow

协作研究：通过自主机器人实验进行数据驱动的微反应工程

• 批准号: 2208489
• 负责人: Kristofer-Roy Reyes
• 金额: $ 24.29万
• 依托单位: SUNY at Buffalo
• 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: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208489&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Driven; Microreaction

Existing experimental strategies often fail to comprehensively explore the reaction universe of new chemicals and materials created with multi-step synthesis procedures. Given the resource-limited nature of experimental searches to find the best reactants and reaction conditions for a certain chemical product, the resulting ad-hoc or uninformed selection of experiments will likely fail to uncover valuable reaction process insights. This collaborative research project will create a science and engineering knowledge framework for accelerated mechanistic reaction studies and synthesis process development of emerging materials and molecules with multi-stage chemistries through a modular approach to chemical synthesis guided by a multi-stage artificial intelligence (AI) strategy. The research team will produce a new data-driven scientific approach to accelerate design and synthesis of high-performing materials and molecules, reducing development time from years to months. Potential applications include energy and chemical technologies, resulting in clear benefits to the nation's prosperity, health, and security. This interdisciplinary research project involves integration of multiple fields including reaction engineering, materials science, and AI. This project will train graduate and undergraduate students in data-driven microreaction engineering and AI-assisted experimentation. The interdisciplinary nature of this collaborative project will enhance participation of students from groups traditionally underrepresented in STEM-related research. Furthermore, the results of this project will positively impact modern engineering education through hands-on lab modules for undergraduate students and tutorial YouTube videos, free to the public and based on the knowledge generated by this research.Implementation of data-driven reaction engineering concepts for emerging solution-processed materials and molecules with multi-stage chemistries require fundamental advancements of AI-guided reaction space exploration, surrogate modeling, and modular experimentation. This project seeks to develop the science base and understanding of modular AI modeling and decision-making strategies for data-driven microreaction engineering through closed-loop modular experimentation. This will enable time- and resource-efficient navigation through the multivariate chemical synthesis space of emerging solution-processed materials and molecules with multi-stage chemistries. The modular AI modeling effort will result in new algorithms that incorporate problem-specific structure and decision-making modalities, enabling autonomous experimentation to move past proof-of-concept demonstrations. Specifically, data-driven microreaction engineering of colloidal quantum dots (QDs) will be targeted, a choice driven by the intriguing size- and composition-tunable optical and optoelectronic properties of QDs as well as multi-stage and process-sensitive synthesis. The results of this collaborative project will advance the state-of-the-art AI-guided chemical synthesis, while lowering the barrier to the use of AI techniques, enabling their broad application among other scientific domains. Furthermore, the modular surrogate modeling of the multi-stage flow reactor systems can be used for evaluation, testing, and validation of kinetics and mechanistic models of nanocrystal nucleation and growth. The autonomous and modular flow synthesis strategy will result in a transferable computational framework that can be applied to other problems in chemical science and engineering, including the models that capture multi-stage, multi-objective process optimization, a problem ubiquitous throughout experimental sciences.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.

现有的实验策略往往无法全面探索多步合成过程产生的新化学品和材料的反应宇宙。鉴于为某种化学产品寻找最佳反应物和反应条件的实验搜索的资源有限性，所产生的临时或不知情的实验选择可能无法发现有价值的反应过程见解。该合作研究项目将创建一个科学和工程知识框架，用于加速机械反应研究和具有多阶段化学的新兴材料和分子的合成工艺开发，通过多阶段人工智能（AI）策略指导的化学合成模块化方法。该研究团队将开发一种新的数据驱动的科学方法，以加速高性能材料和分子的设计和合成，将开发时间从数年缩短到数月。潜在的应用包括能源和化学技术，对国家的繁荣，健康和安全产生明显的好处。这个跨学科的研究项目涉及多个领域的整合，包括反应工程，材料科学和人工智能。该项目将在数据驱动的微反应工程和人工智能辅助实验方面培训研究生和本科生。该合作项目的跨学科性质将提高传统上在STEM相关研究中代表性不足的群体的学生的参与。此外，该项目的成果将通过为本科生提供的实践实验模块和基于本研究产生的知识免费向公众提供的YouTube教程视频，对现代工程教育产生积极影响。针对新兴溶液处理材料和具有多阶段化学反应的分子的数据驱动反应工程概念的实施需要人工智能引导的反应空间探索的根本进步，代理建模和模块化实验。该项目旨在通过闭环模块化实验为数据驱动的微反应工程开发模块化AI建模和决策策略的科学基础和理解。这将使时间和资源有效的导航通过新兴的解决方案处理的材料和分子与多阶段化学的多元化学合成空间。模块化人工智能建模工作将产生新的算法，这些算法将结合特定问题的结构和决策模式，使自主实验能够超越概念验证演示。具体而言，胶体量子点（QD）的数据驱动的微反应工程将成为目标，这是由QD的有趣的尺寸和组成可调的光学和光电特性以及多阶段和工艺敏感的合成驱动的选择。该合作项目的结果将推进最先进的人工智能引导的化学合成，同时降低人工智能技术的使用障碍，使其能够在其他科学领域中广泛应用。此外，多级流动反应器系统的模块化替代建模可用于评估、测试和验证纳米晶体成核和生长的动力学和机理模型。自主和模块化的流程合成策略将产生可转移的计算框架，该计算框架可以应用于化学科学和工程中的其他问题，包括捕获多阶段、多目标过程优化的模型，这个奖项反映了NSF的法定使命，并被认为是值得通过评估使用基金会的智力价值和更广泛的支持。影响审查标准。