面向超快自旋动力学的机器学习赋能实验设计与参数估计
Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics
AI总结：
提出融合机器学习与BOED的XPFS实验设计框架，利用神经网络模拟和自动微分提升参数估计与测量信息效率。
摘要：
先进实验测量对于推动凝聚态与材料物理的理论发展和揭示新现象至关重要，但这类研究常受限于装置资源稀缺和复杂性不断增加。为应对这些局限，我们引入一种将机器学习与贝叶斯最优实验设计（BOED）相结合的方法，并以用于自旋涨落的X射线光子涨落谱（XPFS）测量为例进行展示。该方法采用神经网络模型开展大规模自旋动力学模拟，以在BOED中精确计算分布和效用。我们进一步利用神经网络模型的自动微分能力，实现更稳健、更准确的参数估计。数值基准测试表明，本方法在指导XPFS实验、预测模型参数以及在有限实验时间内获得信息量更高的测量方面表现优越。尽管研究聚焦于XPFS和自旋涨落，该方法仍可适配其他实验，从而促进更高效的数据采集并加速科学发现。
英文摘要：
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.
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