Unknown

Dataset Information

0

Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study.


ABSTRACT: Computer simulations of alloys' properties often require calculations in a large space of configurations in a supercell of the crystal structure. A common approach is to map density functional theory results into a simplified interaction model using so-called cluster expansions, which are linear on the cluster correlation functions. Alternative descriptors have not been sufficiently explored so far. We show here that a simple descriptor based on the Coulomb matrix eigenspectrum clearly outperforms the cluster expansion for both total energy and bandgap energy predictions in the configurational space of a MgO-ZnO solid solution, a prototypical oxide alloy for bandgap engineering. Bandgap predictions can be further improved by introducing non-linearity via gradient-boosted decision trees or neural networks based on the Coulomb matrix descriptor.

SUBMITTER: Midgley SD 

PROVIDER: S-EPMC8279729 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6410806 | biostudies-literature
| S-EPMC9710228 | biostudies-literature
| S-EPMC8363013 | biostudies-literature
| S-EPMC8151263 | biostudies-literature
| S-EPMC7603480 | biostudies-literature
| S-EPMC6916306 | biostudies-literature
| S-EPMC9992102 | biostudies-literature
| S-EPMC8201876 | biostudies-literature
| S-EPMC11325553 | biostudies-literature
| S-EPMC5590652 | biostudies-literature