Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors.
Orb latent space Tc classifier evaluation
Careful evaluation of the classifier model is important so that we can truly understand the capabilities and performance of a Tc predicting model. Particularly important to us is the ability for the m
Orb latent space to Tc prediction
After reading the MatterSim paper, the authors proposed the idea of using the MLFF's latent space as a direct property prediction feature set. Earlier, @will and I had been thinking about using a VAE
Critical temperature prediction models
Literature review of existing studies done on predicting with machine learning.
Superconductor databases
Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
Building on Belli, Zurek & Errea: ML predictions vs quantum nuclear effect descriptors in hydride superconductors
Deep-read and ML analysis of the Belli-Zurek-Errea 2026 npj Computational Materials paper on bonding descriptors for QNEs in hydride superconductors. Ran Tc, Debye, and DOS predictions on 6 hydride systems (4 SB, 2 AB). ML fails to capture QNE direction; the paper's S_a descriptor fills the gap.
Superconductor & Thermoelectric Outreach: 10 prospects, all emails drafted
All 10 personalized outreach emails finalized for superconductors (5) and thermoelectrics (5). Each references specific papers and connects to concrete Ouro resources. Blocked on Resend API access.