Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning
- 1Lynbrook High School 1280 Johnson Avenue San Jose CA 95129 USA
- 2College of Future Information Technology and Key Laboratory of Micro and Nano Photonic Structures (MOE), Fudan University Shanghai 200433 China
- 3School of Physics, East China Normal University Shanghai 200062 China
Abstract
The discovery of stable, high-performance lead-free hybrid perovskite materials remains a major challenge for next-generation photovoltaic technologies. Here, a high-throughput computational framework integrating machine learning, deep learning, and density functional theory (DFT) calculations was adopted to accelerate the discovery and validation of lead-free hybrid perovskites. An optimized XGBoost model was firstly employed to screen approximately 180k charge-neutral perovskite candidates according to formation-energy and bandgap criteria. The resulting 48 kinds of Ge-based candidates were subsequently validated using CGCNN and UPET models, leading to the identification of (CH)4NH2GeGeCl6, C3H4NSGeGeBr6, C5NH12GeGeBr6 and C6OH13GeGeBr6 as promising photovoltaic materials, with HSE + SOC-calculated bandgaps of 1.5137, 1.7515, 1.6778 and 1.1680 eV, respectively. These results demonstrate that integrating data-driven prediction with first-principles validation provides an effective strategy for accelerating the discovery of next-generation photovoltaic materials.
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BibTeX
@article{song2026discovery,
title = {{Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning}},
author = {Carrie Song and R. X. Lian and H. Zhang and Q. Y. Jin},
journal = {RSC advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra07191k},
url = {https://europepmc.org/article/PMC/PMC13690253},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning
AU - Carrie Song
AU - R. X. Lian
AU - H. Zhang
AU - Q. Y. Jin
PY - 2026
DA - 2026/10/22
JO - RSC advances
DO - 10.1039/d6ra07191k
UR - https://europepmc.org/article/PMC/PMC13690253
AB - The discovery of stable, high-performance lead-free hybrid perovskite materials remains a major challenge for next-generation photovoltaic technologies. Here, a high-throughput computational framework integrating machine learning, deep learning, and density functional theory (DFT) calculations was adopted to accelerate the discovery and validation of lead-free hybrid perovskites. An optimized XGBoost model was firstly employed to screen approximately 180k charge-neutral perovskite candidates according to formation-energy and bandgap criteria. The resulting 48 kinds of Ge-based candidates were subsequently validated using CGCNN and UPET models, leading to the identification of (CH)4NH2GeGeCl6, C3H4NSGeGeBr6, C5NH12GeGeBr6 and C6OH13GeGeBr6 as promising photovoltaic materials, with HSE + SOC-calculated bandgaps of 1.5137, 1.7515, 1.6778 and 1.1680 eV, respectively. These results demonstrate that integrating data-driven prediction with first-principles validation provides an effective strategy for accelerating the discovery of next-generation photovoltaic materials.
SN - 2046-2069
ER -