Work

Synergistic Machine Learning Guided Discovery of ABa 3 (BSe 3 ) 2 X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials

Year: 2025

Type: article

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, ABa 3 (BSe 3 ) 2 X (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 [BSe 3 ] unit can be regarded as a potential gene for exploring novel IRFMs. This work may open an avenue for exploring high‐performance materials.

Source: Advanced Science

DOI: 10.1002/advs.202417851

Authors: Yihan Yun, Mengfan Wu, Zhihua Yang, Guangmao Li, Shilie Pan

Institutions: Chinese Academy of Sciences, Xinjiang Technical Institute of Physics & Chemistry, Technical Institute of Physics and Chemistry, University of Chinese Academy of Sciences

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $6,730

Funders: Natural Science Foundation of Xinjiang Province, National Natural Science Foundation of China, Natural Science Foundation of Xinjiang