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Machine learning models predict optimal thermoelectric materials using thermal conductivity ratio

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a data-driven framework using machine learning to accelerate the discovery of thermoelectric materials. The framework identifies materials with a specific lattice-to-total thermal conductivity ratio of approximately 0.5, which is linked to the phonon-glass electron-crystal design concept. This approach screens thousands of compounds to find those with ultralow thermal conductivity and guides optimization strategies for improved thermoelectric performance. AI

IMPACT Provides a new ML-driven screening and optimization framework for materials science, potentially accelerating discovery in energy harvesting technologies.

RANK_REASON Academic paper detailing a new data-driven framework for materials discovery using machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning models predict optimal thermoelectric materials using thermal conductivity ratio

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Academic paper detailing a new data-driven framework for materials discovery using machine learning.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yifan Sun, Zhi Li, Tetsuya Imamura, Yuji Ohishi, Chris Wolverton, Ken Kurosaki ·

    Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design

    arXiv:2511.21213v2 Announce Type: replace-cross Abstract: Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds w…

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