Pipeline links single spin behavior to ensemble magnetic resonance spectra
From Single Spins to Ensembles: A Calibrated Symbolic Regression Pipeline for NV Spectroscopy
Artificial Intelligence
Summary
Measuring magnetic signals from many tiny defects in diamonds is tricky because each defect acts differently. The authors developed a method that builds up the big group’s spectrum by combining models of individual defect behavior while adjusting for differences like local environment and orientation. This approach improves accuracy in predicting collective signals and helps understand how each defect contributes to the whole. It works well on both real and simulated data.
What this means in practice
- •For quantum sensor developers: Model ensemble NV center spectra more accurately by building on single-center spin models, improving magnetic field sensing calibration.
- •For spintronics engineers: Use calibrated ensemble spectral models to design and optimize spin-based devices with better understanding of local spin environment effects.
Authors
Evan Zhang, Zhenghang Zhi, Zewei Wang, Zeheng Wang
Abstract
Characterizing nitrogen-vacancy (NV) ensembles requires connecting their collective spectra to individual centers that experience different local environments. We develop a symbolic-regression pipeline that carries single-center spin structure into a calibrated ensemble model by generating individual spectral responses and combining them with explicit weights. A constrained search selects Hermitian operator terms from single-center optically detected magnetic resonance spectra, fits their coefficients, and retains the selected expressions as source models for the ensemble. Ensemble calibration then accounts for resonance shifts, local disorder, orientation, and optical response while keeping the individual spectral contributions accessible. On public single-center and ensemble data, the zero-field ensemble held-frequency root-mean-square error decreases from 0.008835 to 0.003872 in normalized photoluminescence, a 56.2% improvement over matched uncorrected composition. In controlled QuTiP ensembles of 128 sites spanning four NV orientations, models calibrated at 0 and 3 mT predict an independently held-out 5.17 mT spectrum with errors of 0.000207-0.000228 across three random seeds, comparable to the 0.000200 noise standard deviation. Resolving the simulated response into its individual-center contributions shows how local spectral diversity combines into the ensemble line shape. By retaining the source Hamiltonians while calibrating sample-dependent spectral differences, the pipeline connects local spin physics to collective optical readout and makes the contributions to an ensemble line shape available for physical interpretation.