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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.