Title: Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics Show Chinese title

Title: 机器学习驱动的超快自旋动力学实验设计与参数估计

Abstract: 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. Show Chinese abstract

Abstract: 高级实验测量对于推动凝聚态和材料物理中的理论发展和揭示新现象至关重要，这些领域常常受到设施资源稀缺和复杂性增加的限制。 为解决这些限制，我们引入了一种结合机器学习与贝叶斯最优实验设计（BOED）的方法，以X射线光子涨落光谱（XPFS）测量自旋涨落为例。 我们的方法使用神经网络模型进行大规模自旋动力学模拟，以在BOED中精确计算分布和效用。 神经网络模型的自动微分能力进一步被用于更稳健和准确的参数估计。 我们的数值基准测试展示了该方法在指导XPFS实验、预测模型参数以及在有限实验时间内获得更有信息量的测量方面的优越性能。 尽管专注于XPFS和自旋涨落，但该方法可以适应其他实验，促进更高效的数据收集并加速科学发现。