Researchers at the Texas Materials Institute, Dr. Yuebing Zheng and Dr. Kan Yao, along with other researchers from three additional universities, have developed a powerful new design platform that uses machine learning, computer simulations, and experimental testing to create next-generation thermal metamaterials. These materials are engineered to control how heat is emitted as light, with potential applications in energy efficiency, aerospace, and advanced electronics.
This new framework dramatically expands the possibilities for designing materials by exploring a much larger range of structures and material combinations than ever before. It enables precise control over how materials interact with light at the nanoscale, paving the way for scalable, real-world applications.
By combining artificial intelligence with a deep understanding of materials science, the team has created a flexible and general approach that could be applied to a wide variety of nanophotonic materials — including future innovations in colored emitters, optical devices, and even quantum technologies.
This work represents a major step forward in the field of inverse design, where desired material properties are used to guide the creation of entirely new materials from the ground up.
Read more of their article, "Ultrabroadband and band-selective thermal meta-emitters by machine learning," in Nature.
