中国物理B ›› 2026, Vol. 35 ›› Issue (4): 46102-046102.doi: 10.1088/1674-1056/ae24e7

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Unveiling stable and efficient antiperovskite semiconductors via high-throughput computation and interpretable machine learning

Hao Qu(瞿浩)1,†, Tao Hu(胡涛)1,†, Mingjun Li(李明军)1, Jiangyu Yang(杨江渝)1, Yunyi Zhou(周云逸)1, Shichang Li(李世长)1, Dengfeng Li(李登峰)1,‡, Gang Tang(唐刚)3,§, and Chunbao Feng(冯春宝)1,2,¶   

  1. 1 School of Electronic Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2 Chongqing Key Laboratory of Dedicated Quantum Computing and Quantum Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    3 School of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
  • 收稿日期:2025-08-06 修回日期:2025-11-11 接受日期:2025-11-27 发布日期:2026-04-01
  • 通讯作者: Dengfeng Li, Gang Tang, Chunbao Feng E-mail:lidf@cqupt.edu.cn;gtang@bit.edu.cn;fengcb@cqupt.edu.cn
  • 基金资助:
    Project supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202200632), the National Natural Science Foundation of China (Grant No. 12574089), the Beijing Institute of Technology Research Fund Program for Young Scholars (Grant No. XSQD-202222008), the Beijing National Laboratory for Condensed Matter Physics (Grant No. 2023BNLCMPKF003), and the Guangdong Key Laboratory of Electronic Functional Materials and Devices Open Fund (Grant No. EFMD2023004M).

Unveiling stable and efficient antiperovskite semiconductors via high-throughput computation and interpretable machine learning

Hao Qu(瞿浩)1,†, Tao Hu(胡涛)1,†, Mingjun Li(李明军)1, Jiangyu Yang(杨江渝)1, Yunyi Zhou(周云逸)1, Shichang Li(李世长)1, Dengfeng Li(李登峰)1,‡, Gang Tang(唐刚)3,§, and Chunbao Feng(冯春宝)1,2,¶   

  1. 1 School of Electronic Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    2 Chongqing Key Laboratory of Dedicated Quantum Computing and Quantum Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    3 School of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
  • Received:2025-08-06 Revised:2025-11-11 Accepted:2025-11-27 Published:2026-04-01
  • Contact: Dengfeng Li, Gang Tang, Chunbao Feng E-mail:lidf@cqupt.edu.cn;gtang@bit.edu.cn;fengcb@cqupt.edu.cn
  • Supported by:
    Project supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202200632), the National Natural Science Foundation of China (Grant No. 12574089), the Beijing Institute of Technology Research Fund Program for Young Scholars (Grant No. XSQD-202222008), the Beijing National Laboratory for Condensed Matter Physics (Grant No. 2023BNLCMPKF003), and the Guangdong Key Laboratory of Electronic Functional Materials and Devices Open Fund (Grant No. EFMD2023004M).

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摘要： Nitride antiperovskites have recently been theoretically identified as promising optoelectronic materials, yet their chemical space remains largely unexplored. Here, we employ a high-throughput first-principles screening workflow to systematically investigate the X$_3$BA antiperovskite family. Six candidates that exhibit both structural and dynamical stability together with desirable bandgaps are identified. Electronic-structure calculations reveal that the alkaline-earth-based compounds (e.g., Ca$_3$AsSb, Sr$_3$AsSb, Ba$_3$AsSb) not only possess suitable direct bandgaps and strong optical absorption, but also exhibit favorable ambipolar carrier mobilities and low exciton binding energies ($< 45$ meV). Notably, Sr$_3$AsSb and Ba$_3$AsSb are predicted to achieve theoretical maximum power-conversion efficiencies of 28.1% and 29.4%, respectively. Finally, an interpretable machine-learning model demonstrates that the electronegativity of the A-site anion is the single most influential descriptor governing bandgap trends across the chemical space. This work establishes a data-driven design heuristic and provides a predictive framework for the accelerated discovery of efficient and stable antiperovskite-based optoelectronic materials.

关键词: antiperovskite, physical properties, first-principles calculations, interpretable machine learning

Abstract: Nitride antiperovskites have recently been theoretically identified as promising optoelectronic materials, yet their chemical space remains largely unexplored. Here, we employ a high-throughput first-principles screening workflow to systematically investigate the X$_3$BA antiperovskite family. Six candidates that exhibit both structural and dynamical stability together with desirable bandgaps are identified. Electronic-structure calculations reveal that the alkaline-earth-based compounds (e.g., Ca$_3$AsSb, Sr$_3$AsSb, Ba$_3$AsSb) not only possess suitable direct bandgaps and strong optical absorption, but also exhibit favorable ambipolar carrier mobilities and low exciton binding energies ($< 45$ meV). Notably, Sr$_3$AsSb and Ba$_3$AsSb are predicted to achieve theoretical maximum power-conversion efficiencies of 28.1% and 29.4%, respectively. Finally, an interpretable machine-learning model demonstrates that the electronegativity of the A-site anion is the single most influential descriptor governing bandgap trends across the chemical space. This work establishes a data-driven design heuristic and provides a predictive framework for the accelerated discovery of efficient and stable antiperovskite-based optoelectronic materials.

Key words: antiperovskite, physical properties, first-principles calculations, interpretable machine learning

中图分类号:  (Theory of crystal structure, crystal symmetry; calculations and modeling)

  • 61.50.Ah
71.15.Mb (Density functional theory, local density approximation, gradient and other corrections) 63.20.dk (First-principles theory) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)