Work

Discovery of germanium-based hybrid perovskites with high photovoltaic performance with machine-learning

Year: 2026

Type: article

Abstract: 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.

Source: RSC Advances

DOI: 10.1039/d6ra07191k

Authors: Carrie Song, R. X. Lian, H. Zhang, Q. Y. Jin

Institutions: Fudan University, East China Normal University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,042