Bayesian experimental design and parameter estimation for ultrafast spin dynamics

作者：Zhantao Chen, Peng Cheng, Alexander N. Petsch, Sathya R. Chitturi, Alana Okullo, Sugata Chowdhury, Chun Hong Yoon, Joshua J. Turner · 发表于：Machine Learning Science and Technology · 年份：2023 · DOI：10.1088/2632-2153/ad113a · 被引用次数：2 · 研究领域：Machine Learning in Materials Science、Electron and X-Ray Spectroscopy Techniques、Advanced Electron Microscopy Techniques and Applications

Abstract 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.