5 papers
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…