Synergistic Machine Learning Guided Discovery of ABa3(BSe3)2X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials - Yihan Yun; Mengfan Wu; Zhihua Yang; Guangmao Li; Shilie Pan (Advanced Science)
Abstract Discovering novel infrared functional materials (IRFMs) hold tremendous significance for laser industry. Incorporating artificial intelligence into material discovery has been recognized as a pivotal trend driving advancements in materials science. In this work, an IRFM predictor based on machine learning (ML) is developed for the pre‐selection of the most promising candidates, in which interpretable analyses reveal the prior domain knowledge of IRFMs. Under the guidance of this IRFM predictor, a series of selenoborates, ABa3(BSe3)2X (A = Rb, Cs; X = Cl, Br, I) are successfully predicted and synthesized. Comprehensive characterizations together with first‐principles analyses reveal that these materials exhibit preferred properties of wide bandgaps (2.92 – 3.04 eV), moderate birefringence (0.145 – 0.170 at 1064 nm), high laser‐induced damage thresholds (LIDTs) (4 – 6 Ý AGS) and large second harmonic generation (SHG) responses (0.9 – 1 × AGS). Structure‐property relationship analyses indicate that the [BSe3] unit can be regarded as a potential gene for exploring novel IRFMs. This work may open an avenue for exploring high‐performance materials.