3 research outputs found
Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics
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
Bayesian experimental design and parameter estimation for ultrafast spin dynamics
Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and materials physics, which often suffer from the scarcity of large-scale facility resources, such as x-ray or neutron scattering centers. To address these limitations, we introduce a methodology that leverages the Bayesian optimal experimental design paradigm to efficiently uncover key quantum spin fluctuation parameters from x-ray photon fluctuation spectroscopy (XPFS) data. Our method is compatible with existing theoretical simulation pipelines and can also be used in combination with fast machine learning surrogate models in the event that real-time simulations are unfeasible. Our numerical benchmarks demonstrate the superior performance in predicting model parameters and in delivering more informative measurements within limited experimental time. Our method can be adapted to many different types of experiments beyond XPFS and spin fluctuation studies, facilitating more efficient data collection and accelerating scientific discoveries
On Ultrafast X-ray Methods for Magnetism
With the introduction of x-ray free electron laser sources around the world,
new scientific approaches for visualizing matter at fundamental length and
time-scales have become possible. As it relates to magnetism and
"magnetic-type" systems, advanced methods are being developed for studying
ultrafast magnetic responses on the time-scales at which they occur. We
describe three capabilities which have the potential to seed new directions in
this area and present original results from each: pump-probe x-ray scattering
with low energy excitation, x-ray photon fluctuation spectroscopy, and
ultrafast diffuse x-ray scattering. By combining these experimental techniques
with advanced modeling together with machine learning, we describe how the
combination of these domains allows for a new understanding in the field of
magnetism. Finally, we give an outlook for future areas of investigation and
the newly developed instruments which will take us there