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 ${\mathrm{NiPS}}_3, {\mathrm{NiCl}}_2, {\mathrm{Ca}}_3{\mathrm{LiOsO}}_6$, and ${\mathrm{Fe}}_2{\mathrm{O}}_3$, 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, ${\mathrm{Ca}}_3{\mathrm{LiOsO}}_6$ and ${\mathrm{Fe}}_2{\mathrm{O}}_3$, that were previously missing from the literature.

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• Electronic structure
• Strongly correlated systems
• Electron-correlation calculations
• Exact solutions for many-body systems
• Machine learning
• X-ray emission spectroscopy