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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…
How do defects limit the ultrahigh thermal conductivity of BAs? A first principles study
Mauro Fava, Nakib Haider Protik, Chunhua Li +6
The promise enabled by BAs high thermal conductivity in power electronics cannot be assessed without taking into account the reduction incurred when doping the material. Using firs…
Machine-learning prediction of infrared spectra of interstellar polycyclic aromatic hydrocarbons
Peter Kovacs, Xiaosi Zhu, Jesus Carrete +2
We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many o…
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…
Shortcomings of meta-GGA functionals when describing magnetism
Fabien Tran, Guillaume Baudesson, Jesús Carrete +4
Several recent studies have shown that SCAN, a functional belonging to the meta-generalized gradient approximation (MGGA) family, leads to significantly overestimated magnetic mome…
Chemical trends in the high thermoelectric performance of the pyrite-type dichalcogenides: ZnS2, CdS2 and CdSe2
Tiantian Jia, Jesús Carrete, Georg K. H. Madsen +2
The thermoelectric properties of the three pyrite-type IIB-VIA2 dichalcogenides (ZnS2, CdS2 and CdSe2) are systematically investigated and compared with those of the prototype ZnSe…