Machine learning enabled experimental design and parameter estimation
  for ultrafast spin dynamics
Zhantao Chen Cheng Peng Alexander N. Petsch Sathya R. Chitturi Alana Okullo Sugata Chowdhury Chun Hong Yoon Joshua J. Turner
Advanced experimental measurements are crucial for driving theoretical
developments and unveiling novel phenomena in condensed matter and material
physics, which often suffer from the scarcity of facility resources and
increasing complexities. To address the limitations, we introduce a methodology
that combines machine learning with Bayesian optimal experimental design
(BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS)
measurements for spin fluctuations. Our method employs a neural network model
for large-scale spin dynamics simulations for precise distribution and utility
calculations in BOED. The capability of automatic differentiation from the
neural network model is further leveraged for more robust and accurate
parameter estimation. Our numerical benchmarks demonstrate the superior
performance of our method in guiding XPFS experiments, predicting model
parameters, and yielding more informative measurements within limited
experimental time. Although focusing on XPFS and spin fluctuations, our method
can be adapted to other experiments, facilitating more efficient data
collection and accelerating scientific discoveries.