Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning

TL;DR

This paper introduces a method combining Bayesian optimization and the EDRIXS package to infer Hamiltonian parameters from RIXS spectra, enabling systematic and precise modeling of quantum materials.

Contribution

It presents a novel approach for Hamiltonian inference from RIXS data using active learning, improving accuracy and systematic exploration over traditional methods.

Findings

Successfully inferred Hamiltonian parameters for multiple materials.

Achieved precision comparable to expert manual fitting.

Proposed new atomic models for Ca3LiOsO6 and Fe2O3.

Abstract

Identifying model Hamiltonians is a vital step toward creating predictive models of materials. Here, we combine Bayesian optimization with the EDRIXS numerical package to infer Hamiltonian parameters from resonant inelastic X-ray scattering (RIXS) spectra within the single atom approximation. To evaluate the efficacy of our method, we test it on experimental RIXS spectra of NiPS3, NiCl2, Ca3LiOsO6, and Fe2O3, and demonstrate that it can reproduce results obtained from hand-fitted parameters to a precision similar to expert human analysis while providing a more systematic mapping of parameter space. Our work provides a key first step toward solving the inverse scattering problem to extract effective multi-orbital models from information-dense RIXS measurements, which can be applied to a host of quantum materials. We also propose atomic model parameter sets for two materials, Ca3LiOsO6 and Fe2O3, that were previously missing from the literature.