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

arXiv: https://arxiv.org/abs/2511.21213 · HTML: https://mlsanity.com/paper/2511.21213/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 72 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 1 of 1)

- [Large Language Model-Driven Database for Thermoelectric Materials](https://mlsanity.com/paper/2501.00564/) (2024)

## Abstract (arXiv)

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 kappa. Using a curated dataset of 71,913 entries, we show that high-ZT materials reside not only in the low-kappa regime but also cluster near a lattice-to-total thermal conductivity ratio (kappaL/kappa) of approximately 0.5. This optimal ratio provides a quantitative descriptor for the well-known phonon-glass electron-crystal (PGEC) 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 kappa and kappaL/kappa for screening and guiding the optimization of TE materials. By applying this framework to 104,567 inorganic compounds, we identify 2,522 ultralow-kappa candidates while simultaneously evaluating their proximity to the optimal PGEC regime. A follow-up case study on chemical doping demonstrates how the framework can qualitatively provide optimization strategies that shift pristine materials toward the ideal kappaL/kappa ≈ 0.5 target. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework takes a critical step towards closing the gap between materials discovery and performance enhancement.