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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects.

Mark E Turiansky¹, John L Lyons¹, Noam Bernstein¹

¹US Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, District of Columbia 20375, United States.

|February 24, 2026

Summary

Machine learning potentials significantly accelerate electron-phonon coupling calculations for solid-state defects. This breakthrough overcomes computational bottlenecks, enabling high-level theoretical studies of optical properties and quantum defects.

Keywords:

density functional theory electron−phonon coupling machine learning interatomic potentials optical lineshapes quantum defects

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Area of Science:

Solid-state physics
Quantum optics
Computational materials science

Background:

Optical properties of defects in solids are crucial for applications like gemstone coloration and quantum networks.
Electron-phonon coupling is essential for describing optical transitions but computationally demanding to calculate from first-principles.

Purpose of the Study:

To overcome the computational expense of predicting electron-phonon coupling for solid-state defects.
To demonstrate the efficacy of machine learning interatomic potentials for accurate defect property calculations.

Main Methods:

Utilized machine learning interatomic potentials (MLIPs) to predict electron-phonon coupling.
Fine-tuned MLIPs using atomic relaxation data from first-principles calculations.
Employed hybrid functional calculations for high-accuracy spectral predictions.

Main Results:

Achieved negligible accuracy loss compared to traditional methods.
Demonstrated that routine first-principles data is sufficient for fine-tuning MLIPs.
Resolved fine details of local vibrational mode coupling in the T center in Si luminescence spectrum.

Conclusions:

Machine learning interatomic potentials offer an efficient and accurate alternative for studying defect optical properties.
This approach enables high-level theoretical investigations of defect vibrational properties.
The method accurately predicts spectra and resolves complex coupling phenomena in quantum defects.