Haiyuan Wang^1*, Runhai Ouyang^2*, Wei Chen³, Alfredo Pasquarello¹

1 Chaire de Simulation à l’Echelle Atomique (CSEA), Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.

2 Materials Genome Institute, Shanghai University, Shanghai, 200444, China

3 Institute of Condensed Matter and Nanosciences, Université catholique de Louvain (UCLouvain), B-1348 Louvain-la-Neuve, Belgium.

* Corresponding authors emails: haiyuan.wang@epfl.ch, rouyang@shu.edu.cn

DOI10.24435/materialscloud:ma-ge [version v1]

Publication date: Apr 23, 2024

How to cite this record

Haiyuan Wang, Runhai Ouyang, Wei Chen, Alfredo Pasquarello, High-quality data enabling universality of band-gap descriptor and discovery of photovoltaic perovskites, Materials Cloud Archive 2024.61 (2024), https://doi.org/10.24435/materialscloud:ma-ge

Description

Extensive machine-learning assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band-gap datasets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large dataset of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures, but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr₃, FA₂SnGeBr₆, MA₂AuAuBr₆, FA₂AuAuBr₆, FA₂InBiCl₆, FA₂InBiBr₆, and Ba₂InBiO₆ stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

Materials Cloud sections using this data

Files

File name	Size	Description
0.4_no_nlcc_DSH.zip MD5md5:2e2d59a318922206cc1949217bcedca9	12.9 MiB	The semicore-based pseudopotentials have been purposely generated for this present study, and used for DSH band-gap calculations. The PBE pseudopotentials are generated using exactly the same parameters as for the PseudoDojo ONCVPSP-v0.4 except that nonlinear core correction is disabled. When multiple pseudopotentials are available for a single element, the most stringent one (i.e., with semicore states and a "high" suffix in the filename) are used in the present study.
0.4_fr_no_nlcc_DSH+SOC.zip MD5md5:0f006d6ae2d07158cdff02a50f19344c	11.5 MiB	The semicore-based pseudopotentials have been purposely generated for this present study, and used for DSH+SOC band-gap calculations. The PBE pseudopotentials are generated using exactly the same parameters as for the PseudoDojo ONCVPSP-v0.4 except that nonlinear core correction is disabled. When multiple pseudopotentials are available for a single element, the most stringent one (i.e., with semicore states and a "high" suffix in the filename) are used in the present study.
246_structures.zip MD5md5:8762114a7663aafefac49a0d679c51d4	208.2 KiB	The 246 structures used in this work to construct the DSH band-gap dataset, with all provided structures fully relaxed using the PBE functional.
datasets_246_DSHgap+dielectric_constant.xlsx MD5md5:6d03dca39a36f2bced17cd20c5b6746b	13.2 KiB	The calculated band gaps with the functional of PBE and DSH including the spin-orbit coupling effects, as well as the corresponding dielectric infinity for the 246 materials in our dataset. The calculated band gaps determined using the PBE and DSH functionals, incorporating spin-orbit coupling effects, as well as the corresponding dielectric infinity values, for the 246 materials in our dataset
ML_gap_figure3a&b.xlsx MD5md5:e9e26203ecabd6285f4cc23c9466da56	41.1 KiB	The band gaps obtained using DSH+SOC vs. using the machine learning model [i.e. equation 1 (3D@1C) in the main text], as well as the adopted input features. These results are illustrated in figures 3a and 3b in the main text.
ML_gap_figureS5a&b.xlsx MD5md5:670abcfb233e656c6e56a08dc77a5f8d	40.5 KiB	The band gaps obtained using DSH+SOC vs. using the machine learning model [equation S1 3D@4C(dielectric_infinity)] in the Supporting Information, as well as the adopted input features. These results are illustrated in figures S5a and S5b in the Supporting Information.
Database_I_screening.xlsx MD5md5:1e8a8515c9609155cecf898d0f264bab	78.9 KiB	The 3D@1C machine learning model applied to Materials Project (Database I), including various information such as the screening criteria, predicted band gaps, DSH+SOC band gaps, and their respective input features.
Database_II_screening.xlsx MD5md5:ffc507aea1c697575cf5565506e1e115	207.5 KiB	The 3D@1C machine learning model applied to Database I, including various information such as the screening criteria, predicted band gaps, DSH+SOC band gaps, and their respective input features.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Photoboltaic perovskites Band-gap descriptor machine learning Hybrid functional High quality dataset