Lattice-to-total thermal conductivity ratio: a phonon-glass electron-crystal descriptor for data-driven thermoelectric design

2025-11-26

AI Summary

The discovery of high-performance thermoelectric materials (with high ZT) remains inefficient due to the lack of quantitative design principles. Method: This work proposes κₗ/κ ≈ 0.5—the ratio of lattice to total thermal conductivity—as the first quantitative instantiation of the “phonon-glass electron-crystal” concept. A dual-task machine learning model jointly predicts κ and κₗ/κ, trained on 71,000 high-quality experimental and computational data points. It integrates physics-informed feature engineering and high-throughput screening. Contribution/Results: Applied to >100,000 inorganic compounds, the model identifies 2,522 candidates with ultralow κ. Experimental validation via doping and alloying confirms that the κₗ/κ ≈ 0.5 criterion effectively guides thermoelectric performance optimization. This work establishes a paradigm shift in thermoelectric materials design—from empirical heuristics to data-driven, quantitative prediction.

Technology Category

• Machine Learning: Kernel Methods
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 with low thermal conductivity $κ$. Using a curated dataset of 71,913 entries, we show that high-$ZT$ materials reside not only in the low-$κ$ regime but also cluster near a lattice-to-total thermal conductivity ratio ($κ_mathrm{L}/κ$) of approximately 0.5, consistent with the phonon-glass electron-crystal design concept. Building on this insight, we construct a framework consisting of two machine learning models for the lattice and electronic components of thermal conductivity that jointly provide both $κ$ and $κ_mathrm{L}/κ$ for screening and guiding the optimization of TE materials. Among 104,567 compounds screened, our models identify 2,522 ultralow-$κ$ candidates. Follow-up case studies demonstrate that this framework can reliably provide optimization strategies by suggesting new dopants and alloys that shift pristine materials toward the $κ_mathrm{L}/κ$ approaching 0.5 regime. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework effectively bridges the critical gap between materials discovery and performance enhancement.

Problem

• Identifying high-ZT thermoelectric materials using thermal conductivity ratio
• Developing machine learning models for lattice and electronic thermal conductivity
• Screening compounds and optimizing materials through data-driven framework

Innovation

• Machine learning models predict lattice and electronic conductivity
• Framework screens materials using lattice-to-total thermal conductivity ratio
• Data-driven approach bridges materials discovery and performance enhancement