Machine learning assisted determination of electronic correlations from magnetic resonance

Machine learning assisted determination of electronic correlations from magnetic resonance

In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here, we study how machine lea...

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Veröffentlicht in:arXiv.org 2023-05
Hauptverfasser: Rao, Anantha, Carr, Stephen, Snider, Charles, Feldman, D E, Ramanathan, Chandrasekhar, Mitrović, V F
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Artificial intelligence
Electron spin
Machine learning
Magnetic resonance
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creator Rao, Anantha
Carr, Stephen
Snider, Charles
Feldman, D E
Ramanathan, Chandrasekhar
Mitrović, V F
description In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here, we study how machine learning can extract material parameters and help interpret magnetic response experiments. A low-dimensional representation that classifies the total interaction strength is discovered by unsupervised learning. Supervised learning generates models that predict the spatial extent of electronic correlations and the total interaction strength. Our work demonstrates the utility of artificial intelligence in the development of new probes of quantum systems, with applications to experimental studies of strongly correlated materials.
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subjects Artificial intelligence
Electron spin
Machine learning
Magnetic resonance
title Machine learning assisted determination of electronic correlations from magnetic resonance
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