The Experts below are selected from a list of 825192 Experts worldwide ranked by ideXlab platform
Hiromi Nakai - One of the best experts on this subject based on the ideXlab platform.
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machine learned electron Correlation Model based on frozen core approximation
Journal of Chemical Physics, 2020Co-Authors: Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai, Ryo FujisawaAbstract:The machine-learned electron Correlation (ML-EC) Model is a regression Model in the form of a density functional that reproduces the Correlation energy density based on wavefunction theory. In a previous study [T. Nudejima et al., J. Chem. Phys. 151, 024104 (2019)], the ML-EC Model was constructed using the Correlation energy density from all-electron calculations with basis sets including core polarization functions. In this study, we applied the frozen core approximation (FCA) to the Correlation energy density to reduce the computational cost of the response variable used in machine learning. The coupled cluster singles, doubles, and perturbative triples [CCSD(T)] Correlation energy density obtained from a grid-based energy density analysis was analyzed within FCA and Correlation-consistent basis sets without core polarization functions. The complete basis set (CBS) limit of the Correlation energy density was obtained using the extrapolation and composite schemes. The CCSD(T)/CBS Correlation energy densities based on these schemes showed reasonable behavior, indicating its appropriateness as a response variable. As expected, the computational time was significantly reduced, especially for systems containing elements with a large number of inner-shell electrons. Based on the density-to-density relationship, a large number of data (5 662 500 points), which were accumulated from 30 molecules, were sufficient to construct the ML-EC Model. The valence-electron Correlation energies and reaction energies calculated using the constructed Model were in good agreement with the reference values, the latter of which were superior in accuracy to density functional calculations using 71 exchange–Correlation functionals. The numerical results indicate that the FCA is useful for constructing a versatile Model.
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, ...
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.
Ian F Akyildiz - One of the best experts on this subject based on the ideXlab platform.
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a spatial Correlation Model for visual information in wireless multimedia sensor networks
IEEE Transactions on Multimedia, 2009Co-Authors: Ian F AkyildizAbstract:Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists Correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel spatial Correlation Model for visual information in WMSNs. By studying the sensing Model and deployments of cameras, a spatial Correlation function is derived to describe the Correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed Correlation function and entropy-based framework, a Correlation-based camera selection algorithm is designed. Experimental results show that the proposed spatial Correlation function can Model the Correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the Correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.
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a spatial Correlation Model for visual information in wireless multimedia sensor networks
IEEE Transactions on Multimedia, 2009Co-Authors: Rui Dai, Ian F AkyildizAbstract:Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists Correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel spatial Correlation Model for visual information in WMSNs. By studying the sensing Model and deployments of cameras, a spatial Correlation function is derived to describe the Correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed Correlation function and entropy-based framework, a Correlation-based camera selection algorithm is designed. Experimental results show that the proposed spatial Correlation function can Model the Correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the Correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.
Junji Seino - One of the best experts on this subject based on the ideXlab platform.
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machine learned electron Correlation Model based on frozen core approximation
Journal of Chemical Physics, 2020Co-Authors: Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai, Ryo FujisawaAbstract:The machine-learned electron Correlation (ML-EC) Model is a regression Model in the form of a density functional that reproduces the Correlation energy density based on wavefunction theory. In a previous study [T. Nudejima et al., J. Chem. Phys. 151, 024104 (2019)], the ML-EC Model was constructed using the Correlation energy density from all-electron calculations with basis sets including core polarization functions. In this study, we applied the frozen core approximation (FCA) to the Correlation energy density to reduce the computational cost of the response variable used in machine learning. The coupled cluster singles, doubles, and perturbative triples [CCSD(T)] Correlation energy density obtained from a grid-based energy density analysis was analyzed within FCA and Correlation-consistent basis sets without core polarization functions. The complete basis set (CBS) limit of the Correlation energy density was obtained using the extrapolation and composite schemes. The CCSD(T)/CBS Correlation energy densities based on these schemes showed reasonable behavior, indicating its appropriateness as a response variable. As expected, the computational time was significantly reduced, especially for systems containing elements with a large number of inner-shell electrons. Based on the density-to-density relationship, a large number of data (5 662 500 points), which were accumulated from 30 molecules, were sufficient to construct the ML-EC Model. The valence-electron Correlation energies and reaction energies calculated using the constructed Model were in good agreement with the reference values, the latter of which were superior in accuracy to density functional calculations using 71 exchange–Correlation functionals. The numerical results indicate that the FCA is useful for constructing a versatile Model.
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, ...
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.
Takeshi Yoshikawa - One of the best experts on this subject based on the ideXlab platform.
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machine learned electron Correlation Model based on frozen core approximation
Journal of Chemical Physics, 2020Co-Authors: Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai, Ryo FujisawaAbstract:The machine-learned electron Correlation (ML-EC) Model is a regression Model in the form of a density functional that reproduces the Correlation energy density based on wavefunction theory. In a previous study [T. Nudejima et al., J. Chem. Phys. 151, 024104 (2019)], the ML-EC Model was constructed using the Correlation energy density from all-electron calculations with basis sets including core polarization functions. In this study, we applied the frozen core approximation (FCA) to the Correlation energy density to reduce the computational cost of the response variable used in machine learning. The coupled cluster singles, doubles, and perturbative triples [CCSD(T)] Correlation energy density obtained from a grid-based energy density analysis was analyzed within FCA and Correlation-consistent basis sets without core polarization functions. The complete basis set (CBS) limit of the Correlation energy density was obtained using the extrapolation and composite schemes. The CCSD(T)/CBS Correlation energy densities based on these schemes showed reasonable behavior, indicating its appropriateness as a response variable. As expected, the computational time was significantly reduced, especially for systems containing elements with a large number of inner-shell electrons. Based on the density-to-density relationship, a large number of data (5 662 500 points), which were accumulated from 30 molecules, were sufficient to construct the ML-EC Model. The valence-electron Correlation energies and reaction energies calculated using the constructed Model were in good agreement with the reference values, the latter of which were superior in accuracy to density functional calculations using 71 exchange–Correlation functionals. The numerical results indicate that the FCA is useful for constructing a versatile Model.
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, ...
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.
Yasuhiro Ikabata - One of the best experts on this subject based on the ideXlab platform.
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machine learned electron Correlation Model based on frozen core approximation
Journal of Chemical Physics, 2020Co-Authors: Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai, Ryo FujisawaAbstract:The machine-learned electron Correlation (ML-EC) Model is a regression Model in the form of a density functional that reproduces the Correlation energy density based on wavefunction theory. In a previous study [T. Nudejima et al., J. Chem. Phys. 151, 024104 (2019)], the ML-EC Model was constructed using the Correlation energy density from all-electron calculations with basis sets including core polarization functions. In this study, we applied the frozen core approximation (FCA) to the Correlation energy density to reduce the computational cost of the response variable used in machine learning. The coupled cluster singles, doubles, and perturbative triples [CCSD(T)] Correlation energy density obtained from a grid-based energy density analysis was analyzed within FCA and Correlation-consistent basis sets without core polarization functions. The complete basis set (CBS) limit of the Correlation energy density was obtained using the extrapolation and composite schemes. The CCSD(T)/CBS Correlation energy densities based on these schemes showed reasonable behavior, indicating its appropriateness as a response variable. As expected, the computational time was significantly reduced, especially for systems containing elements with a large number of inner-shell electrons. Based on the density-to-density relationship, a large number of data (5 662 500 points), which were accumulated from 30 molecules, were sufficient to construct the ML-EC Model. The valence-electron Correlation energies and reaction energies calculated using the constructed Model were in good agreement with the reference values, the latter of which were superior in accuracy to density functional calculations using 71 exchange–Correlation functionals. The numerical results indicate that the FCA is useful for constructing a versatile Model.
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree−Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, ...
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machine learned electron Correlation Model based on Correlation energy density at complete basis set limit
Journal of Chemical Physics, 2019Co-Authors: Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi NakaiAbstract:We propose a machine-learned Correlation Model that is built using the regression between density variables such as electron density and Correlation energy density. The Correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total Correlation energy. The numerical examination revealed that the Correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-Correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present Correlation Model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS Correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient Correlation Model.
