20 citations · 54 across the 8 of their papers we have counts for
5 papers · 1 filter
Band Structure Modulation of ZrO2 Nanoparticles for Control of CO Adsorption Properties: A Combined Density Functional Theory - Density Functional Tight Binding Study
Kexin Chen, William Dawson, Aulia Sukma Hutama +4
We present a combined density functional theory (DFT) and density functional tight binding (DFTB) study of zirconia (ZrO2) nanoparticles of experimentally relevant sizes of several…
Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
Sergei Manzhos, Johann Luder, Pavlo Golub +1
Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (OF-DFT) holds the promise of addressing an important bottleneck in large-scale…
A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
Johann Lüder, Manabu Ihara, Sergei Manzhos
We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III-…
Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation
Keisuke Kameda, Takaaki Ariga, Kazuma Ito +2
Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such m…
Machine learning of kinetic energy densities with target and feature averaging: better results with fewer training data
Sergei Manzhos, Johann Lüder, Manabu Ihara
Machine learning of kinetic energy functionals (KEF), in particular kinetic energy density (KED) functionals, has recently attracted attention as a promising way to construct KEFs…