Machine Learning-Driven Insights for Phase-Stable FA <sub><i>x</i></sub> Cs<sub>1-<i>x</i></sub> Pb(I <sub><i>y</i></sub> Br<sub>1-<i>y</i></sub> )<sub>3</sub> Perovskites in Tandem Solar Cells.
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ABSTRACT: The inherent chemical tunability of perovskite materials has spurred extensive research into composition engineering within the perovskite community. However, identifying the optimal composition across a broad range of variations still remains a significant challenge. Conventional trial-and-error methods are prohibitively expensive and environmentally taxing for comprehensive screening. Here, we employed machine learning-accelerated atomic simulation to guide the design of stable perovskite solar cells absorbers. Our approach entailed training of a neural network (NN) potential using data generated from first-principles calculations, yielding a perovskite NN potential exhibiting high accuracy. Utilizing this NN potential, we constructed a phase diagram for FA x Cs1-<
SUBMITTER: Luo R
PROVIDER: S-EPMC12042035 | biostudies-literature | 2025 Apr
REPOSITORIES: biostudies-literature
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