2025-04-29 中国科学院（CAS）
＜関連情報＞
- https://english.cas.cn/newsroom/research_news/phys/202505/t20250506_1042519.shtml
- https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202417851
ABa3(BSe3)2X(A=Rb,Cs;X=Cl,Br,I)の相乗的機械学習による発見： 特性バランスの取れた赤外機能性材料として有望なファミリー 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 Published: 26 April 2025
DOI:https://doi.org/10.1002/advs.202417851
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.
