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.

现有的实验策略通常无法全面探索使用多步合成程序创建的新化学物质和材料的反应世界。鉴于实验搜索的资源有限性质以找到特定化学产品的最佳反应物和反应条件，因此，实验的临时选择或未知的选择可能不会发现有价值的反应过程洞察力。该协作研究项目将创建一个科学和工程知识框架，用于通过多阶段人工智能（AI）策略指导的模块化化学综合方法来加速机械反应研究和新兴材料和分子的合成过程开发，并通过多阶段化学方法开发。研究团队将生产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综合，从而减少了从几年到几个月的开发时间。潜在的应用包括能源和化学技术，从而为国家的繁荣，健康和安全带来了明显的利益。该跨学科研究项目涉及多个领域的整合，包括反应工程，材料科学和AI。该项目将培训来自数据驱动的微反应工程和AI辅助实验的毕业生和本科生。该协作项目的跨学科性质将加强传统上与STEM相关的研究中代表性不足的团体的学生的参与。此外，该项目的结果将通过动手实验室模块对本科生和教程YouTube视频进行积极影响，并根据这项研究产生的知识，并基于数据驱动的反应工程概念。建模和模块化实验。该项目旨在通过闭环模块化实验来开发科学基础和了解数据驱动的微反应工程的模块化AI建模和决策策略。这将使通过多阶段化学的新兴溶液处理材料和分子的多变量化学合成空间进行时间和资源有效的导航。模块化的AI建模工作将导致新算法结合了特定问题的结构和决策方式，从而使自主实验可以移动过去的概念概念验证演示。具体而言，将针对胶体量子点（QD）的数据驱动的微反应工程，这是由QD的有趣的尺寸和组成可 - 可 - 可 - 可 - 可调的光学和光电特性以及多阶段和过程敏感合成的选择。该协作项目的结果将推进最先进的AI引导化学合成，同时降低使用AI技术的障碍，从而在其他科学领域之间进行广泛的应用。此外，多阶段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械模型。自主和模块化流程综合策略将导致可转移的计算框架，该框架可以应用于化学科学和工程的其他问题，包括捕获多个阶段的，多目标过程优化的模型，这是整个实验科学的无处不在的问题。该奖项在整个实验性科学中无处不在。该奖项反映了NSF的法定任务，并通过评估了构成的构成商业的构成群体的构成群体和众所周知。