The recent discovery of superconductivity in bilayer nickelates, induced by pressure or epitaxial strain, raises fundamental questions about how structural tuning governs electronic structure and pairing mechanism. In this talk, I will present a unified approach combining first-principles calculations, cutting-edge machine learning (ML), and angle-resolved photoemission spectroscopy (ARPES) to reveal how structural tuning distinctively governs the electronic structure. Our recent first-principles investigations of strain effects across rare-earth bilayer nickelates show that while compressive strain and pressure both modify apical bond angles similarly, they drive distinct changes in electronic structure. Notably, compressive strain suppresses the energy of the bonding γ bands, with an enhanced orbital energy splitting between the Ni e₉ states. [1] These predictions are corroborated by recent angle-resolved photoemission spectroscopy (ARPES) measurements on superconducting La₂PrNi₂O₇ thin films, which confirm the downward γ band shift under compressive strain. [2] I will also talk about a newly developed approach that could reconstruct the high-dimensional electronic structure from ARPES data with high resolution using the sinusoidal representation network (SIREN) model. [3] Together, this work highlights the synergy of ab initio materials-specific calculations, theoretical modeling, and photon-based spectroscopy in uncovering the structure–superconductivity relationship.

 

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