[EQI] Eddleman Quantum Institute Seminar Series
Abstract: Quantum materials host a wide range of emergent phenomena, such as unconventional superconductivity, charge density wave, quantum spin liquid etc, which often require a synergistic approach that integrates experimental endeavors with theoretical and computational investigations. An example is the recent development of unconventional superconductivity in nickelates. The discoveries of superconductivity in infinite-layer nickelate thin-film with Tc~15K[1], in bilayer La₃Ni₂O₇ bulk under high pressure with Tc~80K[2] and in epitaxial strained La₃Ni₂O₇ thin-film with Tc~40K largely expanded the landscape of high-temperature superconductivity. Understanding the underlying physics of these materials, as well as their differences and similarities with cuprates will shed light on the long-standing mystery of high-Tc cuprates and are crucial for guiding the design of new high-Tc systems.
In this talk, I will present my numerical studies on infinite-layer and bilayer nickelates, focusing on the development of the low-energy effective models from first-principles calculations[4-5], exact diagonalization calculations of resonant inelastic x-ray scattering (RIXS) to interpret key materials properties connecting with experimental characterizations[6], and the investigation of emergent phenomena in the low-energy effective Hamiltonian.[7] In the second part of my talk, I will discuss how advanced AI tools can address challenges in studying quantum materials that are difficult to tackle using traditional approaches. I will talk about how AI-driven approaches can effectively extract underlying physical mechanism from experimental data, predict materials properties, as well as identify new phases in complex high-dimensional phase diagrams.[8]
[1] Li et al, Nature 524, 624 (2019); [2] Sun et al, 621, 493 (2023); [3] Ko et al, Nature 2024; [4] Been et al, PRX 11, 011050 (2021); [5] Bhatta et al, arXiv:2502.01624; [6] Hepting et al, Nature Materials 19, 381 (2020); [7] Peng et al, 108, 245115 (2023); [8] Zhu et al, arXiv:2409.07042.