22 citations · 22 across the 2 of their papers we have counts for
4 papers · 1 filter
Evolutionary computing and machine learning for the discovering of low-energy defect configurations
Marco Arrigoni, Georg K. H. Madsen
Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challen…
Spinney: post-processing of first-principles calculations of point defects in semiconductors with Python
Marco Arrigoni, Georg K. H. Madsen
Understanding and predicting the thermodynamic properties of point defects in semiconductors and insulators would greatly aid in the design of novel materials and allow tuning the…
A comparative first-principles investigation on the defect chemistry of TiO anatase
Marco Arrigoni, Georg K. H. Madsen
Understanding native point defects is fundamental in order to comprehend the properties of TiO anatase in technological applications. Several first-principles studies have been…
Comparing the performance of LDA and GGA functionals in predicting the lattice thermal conductivity of semiconductor materials: the case of AlAs
Marco Arrigoni, Georg K. H. Madsen
In this contribution we assess the performance of two different exchange-correlation functionals in the first-principle prediction of the lattice thermal conductivity of bulk semic…