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.
Ozaki, Variationally optimized atomic orbitals for large-scale electronic structures, Physical Review B67, 155108 (2003)
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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.