Remarkable Optoelectronic Characteristics of Synthesizable Square-Octagon Haeckelite Structures: Machine Learning Materials Discovery

被引:2
|
作者
Alibagheri, Ehsan [1 ]
Ranjbar, Ahmad [2 ,3 ]
Khazaei, Mohammad [1 ,4 ]
Kuehne, Thomas D. [5 ,6 ]
Allaei, S. Mehdi Vaez [1 ,3 ]
机构
[1] Univ Tehran, Dept Phys, North Kargar Ave, Tehran 14395547, Iran
[2] Univ Paderborn, Dynam Condensed Matter, Warburger Str 100, D-33098 Paderborn, Germany
[3] Univ Paderborn, Ctr Sustainable Syst Design, Theoret Chem, Warburger Str 100, D-33098 Paderborn, Germany
[4] Inst Res Fundamental Sci IPM, Sch Nano Sci, Tehran 193955531, Iran
[5] Ctr Adv Syst Understanding CASUS, D-02826 Gorlitz, Germany
[6] Helmholtz Zentrum Dresden Rossendorf, D-02826 Gorlitz, Germany
基金
美国国家科学基金会;
关键词
evolutionary algorithm; first-principles calculations; Haeckelite; machine learning; materials discovery; CRYSTAL-STRUCTURE; GRAPHENE ALLOTROPE; PREDICTION; DENSITY; PERFORMANCE; DATABASE; DESIGN; DFT;
D O I
10.1002/adfm.202402390
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The research explores new compounds with structures similar to square-octagonal beryllium oxide, commonly known as Haeckelites. The goal is to identify semiconducting variations compounds that can be synthesized and show promising potential in optoelectronic devices. Start with 1083 binary Haeckelite structures, and to test the synthesis potential of these materials quickly, develop new descriptors and use machine learning techniques. As a result, it identifies a subset of 350 materials with suitable properties in terms of phase stability and electronic perspective. These materials are further investigated to analyze their electronic structure and phase stability, which are determined through density functional theory calculations. The phase stabilities of the predicted semiconducting Haeckelites are also compared to other binary compounds using an evolutionary structure search algorithm. The comprehensive methodology also includes examining the dynamic stabilities through phonon calculations and mechanical properties through elastic constant calculations. Eventually, 13 new Haeckelite compounds are discovered, demonstrating exceptional stability and performance in electronic tests. Among these compounds, eight have shown remarkable absorption coefficients and are considered promising candidates with high reflectivity. Additionally, they have exhibited high electron mobility. These findings strongly suggest the potential of these compounds for synthesis and their application in optoelectronic devices. This research aims to discover square-octagonal (Haeckelites) semiconducting binary structures. A subset of 350 candidate materials out of 1083 binary Haeckelite structures are identified using machine learning techniques and new descriptors. Through various analyses, including density functional theory calculations and evolutionary structure search, 13 stable Haeckelite compounds with excellent electronic properties are found, some showing high reflectivity and electron mobility, indicating potential for optoelectronic device applications. image
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页数:12
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