Infrared optoelectronic functional materials are essential for applications in lasers, photodetectors, and infrared imaging, forming the technological backbone of modern optoelectronics. Traditionally, the development of new infrared materials has relied heavily on trial-and-error experimental methods. However, these approaches can be inefficient within the extensive chemical landscape, as only a limited number of compounds can achieve a balance of several critical properties simultaneously.
Machine learning approach leads to discovery of high-performance infrared functional materials
