  X-ray absorption spectroscopy (XAS) is a well-established method for in-depth
characterization of the electronic structure due to its sensitivity to the
local coordination and electronic states of the active ions. In practice
hundreds of energy points should be sampled during the XAS measurement, most of
which are redundant and do not contain important information. In addition, it
is also a tedious procedure to estimate reasonable parameters in the atomic
Hamiltonian for mechanistic understanding. We implemented an Adversarial
Bayesian optimization (ABO) algorithm comprising two coupled BOs to
automatically fit the multiplet model Hamiltonian and meanwhile to sample
effectively based on active learning. Taking NiO as an example, for simulated
spectra which can be well fitted by the atomic model, we found that less than
30 sampling points are enough to obtain the complete XAS with the corresponding
crystal field or charge transfer model, which can be selected based on
intuitive hypothesis learning. Further application on the experimental spectra,
it revealed that less than 80 sampling points can already give reasonable XAS
and reliable atomic model parameters. Our ABO algorithm has a great potential
for future application in automated physics-driven XAS analysis and active
learning sampling.
