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

doi:10.1088/1674-1056/ae24e7

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.

Keywords: antiperovskite physical properties first-principles calculations interpretable machine learning

Received: 06 August 2025
Revised: 11 November 2025
Accepted manuscript online: 27 November 2025

PACS:	61.50.Ah	(Theory of crystal structure, crystal symmetry; calculations and modeling)
	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)

Fund: 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).

Corresponding Authors: Dengfeng Li, Gang Tang, Chunbao Feng
E-mail: lidf@cqupt.edu.cn;gtang@bit.edu.cn;fengcb@cqupt.edu.cn

Cite this article:

Hao Qu(瞿浩), Tao Hu(胡涛), Mingjun Li(李明军), Jiangyu Yang(杨江渝), Yunyi Zhou(周云逸), Shichang Li(李世长), Dengfeng Li(李登峰), Gang Tang(唐刚), and Chunbao Feng(冯春宝) Unveiling stable and efficient antiperovskite semiconductors via high-throughput computation and interpretable machine learning 2026 Chin. Phys. B 35 046102

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

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