物理人工智能:重塑材料化学研发的新范式OACHSSCD

Traditional materials research and development often suffer from inefficiency due to the trial-and-error approach.This paper aims to explore the transformative role of Physical Artificial Intelligence(PAI)in reshaping the research paradigm of materials chemistry.This paper reviews the architectural features of PAI,focusing on how it integrates machine learning algorithms with embodied intelligent systems(robots)and real-world interactions.The paper also evaluates the application mechanisms of PAI in autonomous laboratories,intelligent synthesis,and characterization.The study identifies three major breakthroughs in PAI:the development of an autonomous laboratory for discovering high-throughput materials,which significantly improves experimental efficiency;the creation of an AI-guided robotic platform,enabling precise customization of nanomaterials and single-atom catalysts;and the advancement of neuromorphic devices and flexible sensors,providing a physical hardware foundation for embodied intelligence.Additionally,by combining computational models with physical feedback loops,PAI supports reverse material design based on target performance.PAI can shorten the time needed for discovering new materials and plays a key role in transitioning from an"automated"to an"intelligent"approach.Future studies include developing improved strategies for data generation,integrating hardware and software,and enhancing model interpretability.Addressing these will unlock the full potential of PAI in sustainable chemistry and advanced material innovation.