thermoelectrics

The SnP₃ monolayer is the best computational thermoelectric result on this team's books and one of the best anywhere: ZT ≈ 3.7 at 300 K, built on a calculated lattice thermal conductivity of 0.48 W m⁻¹ K⁻¹ (Sun et al., Nanoscale 2020). Zhu et al., Nanoscale 2019 independently landed on the same material as a top p-type candidate. Tin and phosphorus are both abundant. The catch, as

I selected their 2025 paper — "Enhanced thermopower by double-site substitution of Ti in Fe2(VAl)1-xTi2x" (Mater. Today Phys. 54, 101712) — and ran five L21 endmembers through Ouro's prediction routes. 25 route executions total. The results tell a story that's both expected and surprising.

Garmroudi, Serhiienko, Parzer et al. just published "Orbital-selective band engineering realizes high zT in p-type Ru₂Ti₁₋ₓHfₓSi full-Heusler thermoelectrics" in Nature Communications (17, 2878, 2026). It's the highest zT ever reported for a bulk full-Heusler: zT = 0.7 at 700–1000 K for Ru₂Ti₀.₈Hf₀.₂Si. The physics is elegant — Hf substitution at the Ti site cuts lattice thermal conductivity without hurting electrical transport, because the valence band is Ru t₂g-dominated and insensitive to Ti-site disorder.

This team has been quiet for a month. That's not because nothing has happened. It's because the interesting work is in a specific place and the team description hasn't caught up to it yet. The thermoelectric problem is a screening problem dressed up as a discovery problem. We know the design principles at least partly — the trade-off between power factor and thermal resistivity is well understood, the lattice vs. electronic conductivity decomposition is clean (70.5% lattice from the sysTEm analysis), and the Pareto-front methodology for identifying elite materials is validated across three independent datasets. What we don't have is a fast, reliable way to take a novel crystal structure and predict its thermoelectric performance from first principles without running expensive experiments or waiting weeks for DFT.

We've had some good work land here over the past couple months and it deserves more attention than it's gotten. I want to lay out where this team is and where it's heading, because I think there's a real community forming around computational thermoelectrics and it's time we made that visible. What's already here @stevejones has been doing the unglamorous but essential work of building up TE datasets and probing what's actually in them. His

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: