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Research dossier · version 1 · updated Oct 30, 2026

Computational & AI-Driven Superconductor Discovery: State of the Field (October 2026)

Covers ab initio Migdal-Eliashberg calculations, machine-learning models of critical temperature, high-throughput electron-phonon screening and the reported high-pressure hydride record. The main takeaway: first-principles Migdal-Eliashberg calculations reproduce well-studied conventional superconductors such as MgB2, while predictions for new chemistries remain unvalidated until synthesized and independently measured.

3,274 words44 sources29 evidence-mapped claims9 benchmarks17 timeline events✓ citation-verified Oct 30, 2026

AI-assisted synthesis of the cited literature, independently fact-checked against its sources; not peer reviewed.

Contents

  1. Executive summary
  2. Background and core concepts
  3. Timeline of key milestones
  4. State of the art
  5. Ab initio Migdal-Eliashberg calculations
  6. Machine-learning models of critical temperature
  7. High-throughput and surrogate-accelerated screening
  8. Hydrides under pressure
  9. Quantitative benchmarks
  10. Open problems and active debates
  11. Major programs and players
  12. Outlook: what to watch (next 12-18 months)
  13. Key papers
  14. Glossary

Sample findings

  • An anisotropic first-principles Eliashberg calculation reproduced a critical temperature of about 39 K for MgB2 with a coupling constant near 0.61.
  • Standard Eliashberg estimates for undoped LaOFeAs gave a coupling constant of about 0.21 and a maximum Tc near 0.8 K; a coupling 5-6 times larger would be needed to reproduce the observed Tc of 26 K.
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