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

现有的实验策略往往不能全面探索由多步合成过程产生的新化学物质和新材料的反应宇宙。考虑到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的性质，由此产生的临时或不知情的实验选择很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，通过多阶段人工智能（AI）战略指导的模块化化学合成方法，加速新兴材料和具有多阶段化学的分子的机理反应研究和合成过程开发。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，将开发时间从几年缩短到几个月。潜在的应用包括能源和化学技术，为国家的繁荣、健康和安全带来明显的好处。这个跨学科的研究项目涉及反应工程、材料科学、人工智能等多个领域的整合。该项目将培养数据驱动微反应工程和人工智能辅助实验的研究生和本科生。这个合作项目的跨学科性质将加强传统上在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本科生动手实验模块和YouTube教程视频对现代工程教育产生积极影响，并基于本研究产生的知识免费向公众开放。对于新兴的溶液处理材料和具有多阶段化学反应的分子，实现数据驱动的反应工程概念需要人工智能引导的反应空间探索、替代建模和模块化实验的基本进展。本项目旨在通过闭环模块化实验，为数据驱动微反应工程开发模块化人工智能建模和决策策略的科学基础和理解。这将使时间和资源高效导航，通过新兴的溶液处理材料和具有多阶段化学反应的分子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。具体来说，胶体量子点（QDs）的数据驱动微反应工程将成为目标，这是一个由量子点的有趣的尺寸和成分可调的光学和光电子特性以及多阶段和工艺敏感合成驱动的选择。这一合作项目的成果将推动最先进的人工智能引导化学合成，同时降低人工智能技术使用的障碍，使其在其他科学领域得到广泛应用。此外，多级流动反应器系统的模块化替代模型可用于评估、测试和验证纳米晶体成核和生长的动力学和机理模型。自主和模块化流动综合策略将产生一个可转移的计算框架，可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，这是整个实验科学中普遍存在的问题。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.