AI-Driven Flow Chemistry
Developing machine learning algorithms for real-time optimization and control of continuous flow chemical synthesis, enabling autonomous materials discovery.
Overview
Our research in AI-driven flow chemistry focuses on integrating machine learning algorithms with continuous flow reactors to create autonomous systems for chemical synthesis and materials discovery. By combining real-time process monitoring with predictive models, we enable intelligent decision-making that optimizes reaction conditions, predicts outcomes, and accelerates the discovery of novel materials.
Live AI Control Simulation
Conceptual demonstration of an AI agent interacting with a continuous-flow nanoparticle synthesis platform. The visualization illustrates real-time sensing, model predictions, and feedback control
Research Objectives
- •Develop machine learning models for predicting reaction outcomes in flow systems
- •Create autonomous optimization algorithms for real-time process control
- •Build integrated platforms combining flow chemistry with AI-driven analysis
- •Enable high-throughput screening and optimization of chemical reactions
Applications
- •Data-driven synthesis of nanomaterials with tunable properties
- •ML-guided optimization of catalytic and functional materials reactions
- •Discovery of new reaction pathways through adaptive experimentation
- •Scale-up and intensification strategies for industrial flow processes