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Title: Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy
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 Bi2⁢Sr2⁢CaCu2⁢O8+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.  more » « less
Award ID(s):
2239171
PAR ID:
10667272
Author(s) / Creator(s):
; ; ; ; ; ; ;
Publisher / Repository:
Cell Press
Date Published:
Journal Name:
Newton
Volume:
1
Issue:
3
ISSN:
2950-6360
Page Range / eLocation ID:
100066
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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