--- title: "Wake up babe, new thermoelectric dataset just dropped" url: "https://ouro.foundation/posts/stevejones/wake-up-babe-new-thermoelectric-dataset-just-dropped" id: "019e41db-be41-78d6-8a95-93b2a6677ac5" type: "post" --- # Wake up babe, new thermoelectric dataset just dropped Post by @stevejones in #thermoelectrics (all) · 2026-05-19 - Asset id: `019e41db-be41-78d6-8a95-93b2a6677ac5` - Page: https://ouro.foundation/posts/stevejones/wake-up-babe-new-thermoelectric-dataset-just-dropped # Wake up babe, new thermoelectric dataset just dropped > Embedded file: [meme-for-blog](https://ouro.foundation/files/stevejones/meme-for-blog) Ok, the last thermoelectrics dataset I'll be looking at before moving onto modelling/prediction/design phase of work: sysTEm (Systematically Verified Thermoelectric Materials) dataset. It's the highest-quality TE dataset I've worked with so far. Previous TE datasets had issues with missing values, inconsistent units, questionable zT claims. Overall just LLMs not at the place to do reliable data extraction. The datasets we've looked at so far provide a nice overview of the advancement of the technique over the past few years. This most recent one, sysTEm (Tang et al., 2025), fixes some issues by enforcing a simple rule (hence "systematically verified"). Every material must satisfy: within 10% tolerance. If reported zT doesn't match the calculated zT, it's flagged or excluded. This eliminates some inconsistencies that plagued previous datasets. ## **The Dataset** **Coverage:** 8,650 experimental measurements (1991-2024) 1,437 unique materials **Properties:** σ, S, PF, zT, κ_total, κ_lattice, κ_electronic 99.2% completeness for transport properties Top elements (classic heavy-element strategy): Sb, Te, Se, Bi, Cu - heavy chalcogenides and pnictogens dominate O (oxides for high-temp), Sn (emerging systems) **Performance:** Mean zT: 0.42 Maximum: 3.082 (Na₀.₀₃Sn₀.₉₆₅Se at 783K) 841 materials with zT > 1.0 (9.7%) **Temperature effects:** Room temp (300-400K): avg zT = 0.24 Mid-temp (400-700K): avg zT = 0.41 High-temp (700-1000K): avg zT = 0.69 (3× better) ## Pareto Front Analysis I performed the same trade-off analysis I did with NEMAD, identifying materials that optimally balance Power Factor (electrical performance) vs Thermal Resistivity (thermal isolation). > Embedded file: [pareto_front_system_dataset](https://ouro.foundation/files/stevejones/pareto-front-system-dataset) Out of 7,850 materials, 38 are Pareto optimal (0.5%) These elite materials achieve avg zT= 1.31 vs 0.43 overall ## Elite Material Families 1. Bi-Ge-Te systems (3 materials) - Highest zT: 2.35 at 650K - Recent 2022 optimization 2. Ag-Sb-Te + Cd doping (9 materials) - Peak zT: 2.54 at 575K - Systematic composition tuning 3. Ag₂Se-Te composites (6 materials) - All from 2024—very recent - Emerging successor to Bi₂Te₃ 4. Half-Heuslers (Zr-Hf-Ti-Ni-Sn) - Highest power factors (>60 µW/cmK²) 5. Filled skutterudites (CoSb₁₂) - Ultra-low thermal conductivity - Rattler atoms scatter phonons ## Key Insights Thermal conductivity breakdown (2,035 materials with separated contributions): Lattice: 70.5% (1.52 W/mK avg) Electronic: 29.5% (0.64 W/mK avg) → Phonon scattering is the dominant challenge Record holders: - Highest PF (Pareto): FeNb₀.₉Hf₀.₁Sb = 76.05 µW/cmK² - Lowest κ (Pareto): LaCo₀.₅(Ni₀.₅Fe₀.₅)₀.₅O₃ = 0.07 W/mK The fundamental trade-off: High PF materials tend to have high κ (metals conduct heat). Low κ materials often sacrifice electrical performance. Pareto optimal materials navigate this through: - Nanostructuring (Ag₂Se composites) - Smart alloying (Cd in AgSbTe₂, Ge in Bi₂Te₃) - Rattler atoms in cage structures - Heavy element combinations ## **What Makes sysTEm Different** Validation: Every material with calculated zT +/- 10% of recorded. Provenance: Every point includes DOI, year, source, temperature Completeness: 99%+ coverage vs 65-75% in other datasets Recency: 50% from 2020+, capturing latest research This is by far the best thermoelectric dataset available. Perfect for ML, screening, benchmarking, and understanding design principles. ### Access Citation: Tang LZ, Purdy L, Mohanty T, Ng LWT, Sparks TD. Systematically Verified Experimental Thermoelectric Dataset. ChemRxiv. 2025; doi:10.26434/chemrxiv-2025-4gxmc 🤖 In collaboration with Sonnet ## Assets in this post - [meme-for-blog](https://ouro.foundation/files/stevejones/meme-for-blog) (file, id `29f5e988-cf9a-41ea-80ed-f35b94806b22`) - [pareto_front_system_dataset](https://ouro.foundation/files/stevejones/pareto-front-system-dataset) (file, id `b603e648-8b2c-4f8b-889c-aadcb11c0983`)