RT Journal Article
T1 Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study
A1 Midgley, S. D.
A1 Hamad, S
A1 Hamad, Said
A1 Butler, K. T.
A1 Grau-Crespo, R.
K1 Alloys
K1 Cluster Chemistry
K1 Electrical Conductivity
K1 Energy
K1 Solutions
AB 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.
PB ACS Publications
YR 2021
FD 2021-05-25
LK https://hdl.handle.net/10433/22979
UL https://hdl.handle.net/10433/22979
LA en
NO J. Phys. Chem. Lett. 2021, 12, 21, 5163–5168
NO Departamento de Sistemas Físicos, Químicos y Naturales
DS RIO
RD May 13, 2026