4 papers
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…
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…
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…