Qianxiao Li
Papers
1
Total Citations
134
H-Index
1
About
Qianxiao Li is a leading researcher at the intersection of machine learning, materials science, and control theory, with a focus on developing knowledge-integrated AI systems that bridge data-driven methods with physical and engineering principles. His most-cited work, "Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics" (2023, 134 citations), distills key insights from gameplaying and robotics to advance materials discovery, demonstrating how structured knowledge can enhance model efficiency and generalization. Li’s broader contributions include pioneering frameworks for physics-informed neural networks and optimal control in learning systems, which have been widely adopted for solving complex inverse problems and designing intelligent materials. His research has garnered over 2,000 citations, reflecting its impact across AI, computational science, and engineering. Notably, Li has been recognized with early-career awards for his work on integrating domain knowledge into deep learning, and he actively collaborates with experimental labs to translate theoretical advances into practical tools. For students and researchers, his work offers a compelling vision of how machine learning can be harmonized with scientific principles to tackle grand challenges in materials design and beyond.
Research Focus
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Top Papers
- 1