从太赫兹二维相干光谱学习自旋哈密顿量
Learning Spin Hamiltonians from Terahertz Two-Dimensional Coherent Spectroscopy
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中文总结 AI 辅助
本研究提出监督机器学习框架，结合THz-2DCS光谱与双亚晶格自旋模型，实现量子材料有效哈密顿量参数的精准推断，为量子材料涌现性质研究提供实验驱动的新途径。
中文摘要 AI 辅助
有效哈密顿量将量子材料中的微观相互作用与可测量的集体行为联系起来，但直接从实验确定其参数仍是一项具有挑战性的逆问题。我们提出一种监督机器学习框架，可从非线性太赫兹二维相干光谱（THz-2DCS）中推断哈密顿量参数。该框架包含三个关键部分：校准的正向模型，能从候选哈密顿量生成光谱；通用预处理流程，将模拟光谱与实验光谱映射到同一表征空间；神经网络，学习从光谱指纹到微观参数的逆映射。我们采用含交换作用、Dzyaloshinskii-Moriya相互作用、各向异性及阻尼的双亚晶格Landau-Lifshitz-Gilbert自旋模型，对稀土正铁氧体验证该方法。合成基准测试表明，非线性光谱编码了线性响应无法固定的参数，推断精度与物理光谱灵敏度匹配，且通过使用多个脉冲间延迟提升了抗噪声鲁棒性。将该方法应用于Sm₀.₄Er₀.₆FeO₃的实验THz-2DCS数据，推断出的参数可生成物理上合理的正向模拟，而残留差异则明确了简化模型的局限性。这些结果证实THz-2DCS是用于有效哈密顿量推断和模型优化的高数据量平台，可实现由实验驱动的微观相互作用识别，为理解、预测并最终控制量子材料的涌现性质奠定基础。
英文摘要
Effective Hamiltonians connect microscopic interactions to measurable collective behavior in quantum materials, but determining their parameters directly from experiment remains a challenging inverse problem. We introduce a supervised machine-learning framework that infers Hamiltonian parameters from nonlinear terahertz two-dimensional coherent spectra. A calibrated forward model generates spectra from candidate Hamiltonians, a common preprocessing pipeline maps simulated and experimental spectra into the same representation, and a neural network learns the inverse map from spectral fingerprints to microscopic parameters. We demonstrate the approach for rare-earth orthoferrites using a two-sublattice Landau--Lifshitz--Gilbert spin model with exchange, Dzyaloshinskii--Moriya interaction, anisotropies, and damping. Synthetic benchmarks show that nonlinear spectra encode parameters beyond those fixed by the linear response, with inference accuracy tracking the physical spectral sensitivity and robustness against noise improved by using multiple inter-pulse delays. Applied to experimental THz-2DCS data from Sm$_{0.4}$Er$_{0.6}$FeO$_3$, the inferred parameters yield physically reasonable forward simulations, while remaining discrepancies identify limitations of the reduced model. These results establish THz-2DCS as a data-rich platform for effective-Hamiltonian inference and model refinement, enabling experimentally driven identification of microscopic interactions while providing a foundation for understanding, predicting, and ultimately controlling the emergent properties of quantum materials.
