8 citations · 20 across the 8 of their papers we have counts for
8 papers
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…
ML and AI for density functional theory: different priorities for Kohn-Sham and orbital-free DFT, for electronic and nuclear DFT
Xin-Hui Wu, Sergei Manzhos
We overview similarities and, importantly, differences in computational bottlenecks and accuracy requirements that can be addressed with machine learning (ML) and artificial intell…
Neural networks for neurocomputing circuits: a computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties
Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh +2
Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a di…
Gaussian Process Regression -- Neural Network Hybrid with Optimized Redundant Coordinates
Sergei Manzhos, Manabu Ihara
Recently, a Gaussian Process Regression - neural network (GPRNN) hybrid machine learning method was proposed, which is based on additive-kernel GPR in redundant coordinates constru…
Nuclear Mass Predictions Using a Neural Network with Additive Gaussian Process Regression-Optimized Activation Functions
H. X. Liu, S. Manzhos, X. H. Wu
Nuclear masses are machine-learned as a function of proton and neutron numbers. The neural network with additive Gaussian process regression-optimized activation functions (GPR-NN)…
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…