Networking autonomous material exploration systems through transfer learning

Abstract Autonomous material exploration systems that integrate robotics, material simulations, and machine learning have advanced rapidly in recent years. Although their number continues to grow, these systems currently operate in isolation, limiting the overall efficiency of autonomous material discovery. In analogy to how human researchers advance materials science by sharing knowledge and collaborating, autonomous systems can also benefit from networking and knowledge exchange. Here, we propose a framework in which multiple autonomous material exploration systems form a network via transfer learning, selectively utilizing relevant knowledge from other systems in real time. We demonstrate this approach using three distinct autonomous systems and show that such networking significantly enhances the efficiency of material discovery. Our results suggest that the proposed framework can enable the development of large-scale autonomous material exploration networks, ultimately accelerating progress in material development.