通过光电子能谱的可解释机器学习探测热力学相变
Detecting Thermodynamic Phase Transition via Explainable Machine Learning of Photoemission Spectroscopy
AI总结：
本文提出结合角分辨光电子能谱与域对抗神经网络的数据驱动方法，仅凭单粒子光谱识别热力学相变，在Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$中达97.6%准确率，并揭示带隙内谱重的关键作用。
摘要：
在低维材料中，由于强涨落和低探测体积，识别电子相（如超导）的热力学特征具有挑战性。光谱方法常被用于识别新的体相，但其主要的可测量量——电子能隙——在低维和涨落系统中不再是有效的序参量。我们将角分辨光电子能谱与域对抗神经网络相结合，报告了一种仅基于单粒子光谱来识别热力学相变的数据驱动方法。我们在具有强超导涨落的铜氧化物超导体Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$中展示了97.6$\%$的准确率。该模型通过利用几乎取之不尽的模拟数据，显著弥补了实验数据的稀缺性。此外，其可解释性揭示了带隙内谱重在探测相位涨落和热力学转变中的关键作用。我们的工作指出了涨落序的光谱特征，并使光谱学能够用于低维和强耦合体系的机器学习辅助材料发现。
英文摘要：
Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity -- electronic energy gaps -- is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6$\%$ accuracy in cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.
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