Machine learning interatomic potentials fine-tuned on first-principles relaxation data accurately reproduce phonon spectra and optical lineshapes for defects, matching explicit calculations and experiments.
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cond-mat.mtrl-sci 2years
2025 2verdicts
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An approximation technique estimates electron-phonon coupling in solid-state defects from excited-state forces computed at the ground-state geometry, benchmarked on three defect systems and shown to bound the accepting-mode Huang-Rhys factor.
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Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
Machine learning interatomic potentials fine-tuned on first-principles relaxation data accurately reproduce phonon spectra and optical lineshapes for defects, matching explicit calculations and experiments.
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Approximate Excited-State Potential Energy Surfaces for Defects in Solids
An approximation technique estimates electron-phonon coupling in solid-state defects from excited-state forces computed at the ground-state geometry, benchmarked on three defect systems and shown to bound the accepting-mode Huang-Rhys factor.