Machine learning potential-assisted design of thermoelectricvperformance in anharmonic CsPbBr3 and CsPbI3†

Yuanyuan Chen; Shuo Cao; Yu Bao; Keke Song; Ping Qian; Yanjing Su

doi:10.1039/d6tc00498a

January 1, 2026Journal of Materials Chemistry C

Machine learning potential-assisted design of thermoelectricvperformance in anharmonic CsPbBr3 and CsPbI3†

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Key Points

The study aims to design and analyze the thermoelectric properties of halide perovskites using machine learning and DFT.
Utilized machine learning approaches to analyze halide perovskites.
Employed density functional theory for structural stability evaluations.
Trained a neuroevolution potential to enhance predictive accuracy.
Found improved predictions of thermoelectric performance in CsPbBr3 and CsPbI3.
Demonstrated significant structural stability in the studied materials.
Confirmed that machine learning can optimize material design for better thermoelectric efficiency.

Implication

This research investigates thermoelectric properties in halide perovskites using machine learning and DFT, indicating advanced materials design techniques.

Abstract

The structural stability and thermoelectric properties of halide perovskites were investigated using a combination of machine learning approaches and density functional theory (DFT). A neuroevolution potential (NEP) was trained via...

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69c6201515a0a509bde187ad https://doi.org/10.1039/d6tc00498a

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Machine learning potential-assisted design of thermoelectricvperformance in anharmonic CsPbBr3 and CsPbI3†

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine learning potential-assisted design of thermoelectricvperformance in anharmonic CsPbBr3 and CsPbI3†

Key Points

Implication

Abstract

Discussion

Cite This Study

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