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    Machine-learning-assisted density functional theory for predicting transparent conducting materials

    Yang Huang1,*, Gui Wang2,*, Shaogang Xu3, Yiguo Xu4, Da Li5,†, Menglin Huang6,‡, Lars Samuelson1, and Xiao Wei Sun1,§

    • *These authors contributed equally to this work.
    • †Contact author: lida@szu.edu.cn
    • ‡Contact author: menglinhuang@fudan.edu.cn
    • §Contact author: sunxw@sustech.edu.cn

    Phys. Rev. Applied 25, 014071 – Published 29 January, 2026

    DOI: https://doi.org/10.1103/br17-vmg2

    Abstract

    Despite the success of n-type transparent conducting oxides (TCOs), there is a notable scarcity of new p-type TCOs and transparent conducting materials (TCMs) in optoelectronics. We utilized 10 333 Heyd-Scuseria-Ernzerh (HSE) band gap data points for transfer learning to enhance the Perdew-Burke-Ernzerhof (PBE) band gap model, resulting in a reliable HSE model that aligns closely with 640 calculated values. We screened 518 materials with band gaps of 3.0 eV to 4.3 eV from 5276 compounds in the Materials Project, identifying two potential p-type (nine potential n-type) transparent conductors via high-throughput density functional theory. For n-type TCMs, cations of AlSbO4, CaIn2O4, Mg2SnO4, In2Ge2O7, and MgGeN2 possess a closed-shell electronic ns0 configuration, with a forbidden dipole transition between the valence band maximum (VBM) and the conduction band minimum (CBM). Furthermore, p-type TCMs KGeCl3 and RbSnCl3 feature anions with delocalized p orbitals that hybridize with cations’ (n−1)d10ns2 configurations, leading to a low hole effective mass. Moreover, RbSnCl3 was identified as a potential p-type material based on intrinsic point defect properties analyzed using the HSE06 hybrid functional. This work expands the database of inorganic transparent conductors, necessitating further validation.

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