AI 中文总结
研究人员开发基于CNN的方法，结合“Alice-Bob”并行网络从FORCs磁指纹中提取微磁哈密顿量，通过闭环验证可准确重构磁测量数据，为揭示复杂磁系统自旋行为提供了机器学习辅助的鲁棒方案。
AI 中文摘要
直接从磁测量中提取本征磁哈密顿量颇具挑战，原因在于参数空间的高维度以及系综平均引发的简并性。在此，我们引入一组深度卷积神经网络（CNN），直接从一阶反转曲线（FORCs）编码的磁指纹中提取完整的唯象微磁哈密顿量。我们通过闭环验证对该方法进行了评估，针对模拟和实验FORCs重新构建了输入磁测量数据。为减少假阳性结果，我们部署了一个“Alice-Bob”并行网络，仅基于FORCs中的信息量化预测不确定性，无需任何额外的真值知识。该框架提供了一种鲁棒的、机器学习辅助的方法，用于揭示复杂磁系统中潜在的自旋行为。
英文摘要
Extracting intrinsic magnetic Hamiltonians directly from magnetometry is challenging due to the high dimensionality of the parameter space and the degeneracy induced by ensemble averaging. Here, we introduce a collection of deep convolutional neural networks (CNNs) to extract the full phenomenological micromagnetic Hamiltonian directly from the magnetic fingerprints encoded within First-Order Reversal Curves (FORCs). We validate this approach via closed-loop verification, re-creating the input magnetometry for both simulated and experimental FORCs. To mitigate false positives, we deploy an `Alice--Bob' parallel network that quantifies prediction uncertainty based on solely the information in FORCs without any additional ground-truth knowledge. This framework provides a robust, machine-learning-assisted approach to unravel the underlying spin behaviors in complex magnetic systems
