Three things happened in the past month that deserve attention from anyone working on superconductor discovery. None of them solve the problem, but all three move the pieces into more interesting positions.
University of Houston breaks the ambient-pressure Tc record. Paul Chu and Xiaofeng Deng's team crushed the 30-year record using pressure quenching on superhydrides, then published a companion perspective in PNAS laying out six approaches to close the remaining ~140°C gap to room temperature. Deng is one of the people we've been hoping to get into this team. The pressure-quench idea is genuinely novel because it sidesteps the diamond-anvil-cell problem: you enhance the superconducting state under pressure, then lock the structure in. The six pathways in that perspective paper are worth reading as a whole because they collectively argue that the field should stop chasing one miracle compound and start engineering the state systematically.
Chalmers nails the thin-film problem with nanofacets. Lombardi's group published in Nature Communications that YBa₂Cu₃O₇₋δ films on nanofaceted substrates show enhanced critical current and magnetic field tolerance. The result is small in the sense that it's one material system, but it's large in consequence because the nanofacet engineering approach is portable to other cuprates. If your superconductor works at low T but can't survive the magnetic fields in a device, this is the strategy to study.
A new benchmark dataset built for AI-driven Tc prediction. The HTSC-2025 dataset on arXiv (2506.03837v2) collected ambient-pressure high-Tc superconductors with curated properties specifically for training ML models. It includes 144 new high-pressure candidates identified by ML screening, plus Li₂AuH₆ at 140 K from InvDesFlow, which apparently blows past the McMillan limit at ambient pressure. The field has been starving for this kind of curated, ML-ready dataset. The previous benchmark sets were either too noisy or too narrow to train on meaningfully.
These three results are all pointing in the same direction: the superconductor problem is becoming an engineering problem rather than a discovery problem. The question is shifting from "can we find a compound that superconducts" to "can we engineer the lattice environment to stabilize the state." And engineering problems are solvable with data and iteration.
That is what this team is built for. We have the TE API for rapid screening, crystal generation routes with GPSK-300, and validation infrastructure that
If you're working on Tc prediction, thin-film engineering, or screening datasets, the work you produce belongs on this team. The infrastructure is already running. What it needs is people who know what to point it at.
Recent breakthroughs worth watching and where Ouro fits in