A machine learning Hamiltonian model trained on small 95-atom supercells enables linear-scaling structural relaxations and accurate formation energy predictions for oxygen vacancies in larger supercells of amorphous SiO2, with deviations below 50 meV from DFT due to error cancellation.
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cond-mat.mtrl-sci 2years
2026 2verdicts
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Symmetry-adapted analysis of screw dislocations in GaN identifies band-connectivity constraints, dipole selection rules, and a core piezoelectric effect that suppresses radiative recombination in favor of non-radiative capture.
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Machine learning Hamiltonian enables scalable and accurate defect calculations: The case of oxygen vacancies in amorphous SiO$_2$
A machine learning Hamiltonian model trained on small 95-atom supercells enables linear-scaling structural relaxations and accurate formation energy predictions for oxygen vacancies in larger supercells of amorphous SiO2, with deviations below 50 meV from DFT due to error cancellation.
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Symmetry Adapted Analysis of Screw Dislocation: Electronic Structure and Carrier Recombination Mechanisms in GaN
Symmetry-adapted analysis of screw dislocations in GaN identifies band-connectivity constraints, dipole selection rules, and a core piezoelectric effect that suppresses radiative recombination in favor of non-radiative capture.