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

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.