자율화학실험실

which transforms desired properties and performance targets into promising molecular structures, materials and synthesis conditions. and data-driven decision-making. Experimental results can then be used to refine computational models and guide subsequent design. chemical discovery—moving from understanding how chemical systems behave toward designing how they should behave. Robotic platforms carry out synthesis, handling and characterization with consistent control over each experimental step. Closing the loop between experiment, analysis and intelligent decision-making. Robotic systems perform synthesis and characterization, while AI analyzes the results and selects the next experiment. By repeating this closed-loop process, the laboratory can autonomously explore experimental conditions and move toward desired research objectives. AI learns from experimental outcomes and determines which conditions should be explored next to accelerate discovery. The orchestration system coordinates robots, instruments and data flows, linking individual tasks into a continuous experimental workflow. Our lab aims to accelerate discovery in chemistry and materials science by integrating AI, automation, and molecular simulation. A central focus is the development of self-driving laboratories that autonomously design, perform, and analyze experiments to efficiently explore complex chemical and materials spaces. In parallel, we develop AI methods that connect experimental observations with theoretical predictions, helping to close the longstanding gap between computational models and real-world laboratory systems. By combining data-driven learning, chemistry/physics-based modeling, and autonomous experimentation in a closed-loop framework, our research seeks to establish a new paradigm fo