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Manabu Ihara

Showing results (1-10 of 21) with videos related to

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The Journal of Chemical Physics|February 1, 2023
The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfacesSergei Manzhos, Manabu Ihara
The Journal of Chemical Physics|December 1, 2023
A controlled study of the effect of deviations from symmetry of the potential energy surface (PES) on the accuracy of the vibrational spectrum computed with collocationSergei Manzhos, Manabu Ihara
Physical Chemistry Chemical Physics : PCCP|May 11, 2022
Computational vibrational spectroscopy of molecule-surface interactions: what is still difficult and what can be done about itSergei Manzhos, Manabu Ihara
The Journal of Chemical Physics|January 8, 2024
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimensionSergei Manzhos, Manabu Ihara
The Journal of Physical Chemistry. A|September 12, 2023
Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process RegressionSergei Manzhos, Manabu Ihara
Physical Chemistry Chemical Physics : PCCP|May 16, 2023
Clarifying the effects of nanoscale porosity of silicon on the bandgap and alignment: a combined molecular dynamics-density functional tight binding computational studyPanus Sundarapura, Sergei Manzhos, Manabu Ihara
Physical Chemistry Chemical Physics : PCCP|December 23, 2022
Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionalitySergei Manzhos, Shunsaku Tsuda, Manabu Ihara
Journal of Chemical Theory and Computation|March 28, 2023
Using Collocation to Solve the Schrödinger EquationSergei Manzhos, Manabu Ihara, Tucker Carrington
The Journal of Chemical Physics|December 19, 2023
Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training dataSergei Manzhos, Johann Lüder, Manabu Ihara
The Journal of Chemical Physics|September 16, 2025
Erratum: "Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data" [J. Chem. Phys. 159, 234115 (2023)]Sergei Manzhos, Johann Lüder, Manabu Ihara
Pageof 3

Showing results (1-10 of 21) with videos related to

Sort By:
Pageof 3
The Journal of Chemical Physics|February 1, 2023
The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfacesSergei Manzhos, Manabu Ihara
The Journal of Chemical Physics|December 1, 2023
A controlled study of the effect of deviations from symmetry of the potential energy surface (PES) on the accuracy of the vibrational spectrum computed with collocationSergei Manzhos, Manabu Ihara
Physical Chemistry Chemical Physics : PCCP|May 11, 2022
Computational vibrational spectroscopy of molecule-surface interactions: what is still difficult and what can be done about itSergei Manzhos, Manabu Ihara
The Journal of Chemical Physics|January 8, 2024
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimensionSergei Manzhos, Manabu Ihara
The Journal of Physical Chemistry. A|September 12, 2023
Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process RegressionSergei Manzhos, Manabu Ihara
Physical Chemistry Chemical Physics : PCCP|May 16, 2023
Clarifying the effects of nanoscale porosity of silicon on the bandgap and alignment: a combined molecular dynamics-density functional tight binding computational studyPanus Sundarapura, Sergei Manzhos, Manabu Ihara
Physical Chemistry Chemical Physics : PCCP|December 23, 2022
Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionalitySergei Manzhos, Shunsaku Tsuda, Manabu Ihara
Journal of Chemical Theory and Computation|March 28, 2023
Using Collocation to Solve the Schrödinger EquationSergei Manzhos, Manabu Ihara, Tucker Carrington
The Journal of Chemical Physics|December 19, 2023
Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training dataSergei Manzhos, Johann Lüder, Manabu Ihara
The Journal of Chemical Physics|September 16, 2025
Erratum: "Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data" [J. Chem. Phys. 159, 234115 (2023)]Sergei Manzhos, Johann Lüder, Manabu Ihara
Pageof 3