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TT: Fachverband Tiefe Temperaturen
TT 56: Superconductivity: Theory I
TT 56.10: Vortrag
Mittwoch, 11. März 2026, 17:30–17:45, CHE/0089
Understanding and enhancing superconductivity in cuprates with low-energy Hamiltonians and explicit machine learning — •Jean-Baptiste Morée1 and Ryotaro Arita1,2 — 1RIKEN Center for Emergent Matter Science, Wako, Saitama 351-0198, Japan — 2Department of Physics, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
Cuprate superconductors exhibit a wide range of transition temperatures Tc ≈ 6–166 K despite sharing a common electronic structure dominated by a Cu 3dx2−y2–O 2pσ antibonding orbital. Ab initio low-energy Hamiltonians combined with many-variable variational Monte Carlo have shown that Tc is primarily controlled by the nearest-neighbor hopping |t1| and the ratio u = U/|t1| (with U the onsite effective Coulomb repulsion), with only minor influence from longer-range terms. Applied pressure enhances Tc mainly by increasing |t1|.
In this talk, I present recent progress [1] on the material dependence of these parameters using a new explicit, interpretable machine-learning approach. By analyzing structural and chemical descriptors across 36
cuprates, the algorithm reveals that |t1| increases when ionic radii in the block layer are reduced, while u can be tuned through the ionic charges. These results provide simple, physically transparent guidelines for designing cuprates with enhanced superconducting properties.
[1] J.-B. Morée and R. Arita, Phys. Rev. B 110, 014502 (2024).
Keywords: Cuprate superconductors; Unconventional superconductivity; Ab initio downfolding; Low-energy effective Hamiltonians; Interpretable machine learning