The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
J. J. Dannenberg - One of the best experts on this subject based on the ideXlab platform.
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a comparison of the behavior of Functional Basis set combinations for hydrogen bonding in the water dimer with emphasis on Basis set superposition error
Journal of Computational Chemistry, 2011Co-Authors: Joshua A. Plumley, J. J. DannenbergAbstract:We evaluate the performance of ten Functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 Basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of Functionals and Basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density Functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller Basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest Basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different Functionals. Small Basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller Basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a Functional that predicts a weak interaction with the largest Basis set. As many applications are complex systems and require economical calculations, we suggest the following Functional/Basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP.
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A comparison of the behavior of Functional/Basis set combinations for hydrogen-bonding in the water dimer with emphasis on Basis set superposition error.
Journal of computational chemistry, 2011Co-Authors: Joshua A. Plumley, J. J. DannenbergAbstract:We evaluate the performance of ten Functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 Basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of Functionals and Basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density Functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller Basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest Basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different Functionals. Small Basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller Basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a Functional that predicts a weak interaction with the largest Basis set. As many applications are complex systems and require economical calculations, we suggest the following Functional/Basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP.
S.r. Leclair - One of the best experts on this subject based on the ideXlab platform.
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ICNN - Orthogonal Functional Basis neural network for Functional approximation
Proceedings of International Conference on Neural Networks (ICNN'97), 1Co-Authors: C.l.p. Chen, Y. Cao, S.r. LeclairAbstract:Subset selection is a well-known technique for generating an efficient and effective neural network structure. The technique has been combined with regularization to improve the generalization performance of a neural network. In this paper, we show an incongruity involving subset selection and regularization. We present an approach to solve this dissonance wherein our subset selection is derived from a combination of Functional Basis. A more efficient training convergence speed is shown using the new Basis which is derived from an 'orthogonal-Functional-Basis' transformation. With this transformation we propose a new orthogonal Functional Basis neural network structure which is not only more computationally tractable but also gives better generalization performance. Simulation studies are presented that demonstrate the performance, behavior, and advantages of the proposed network.
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Material structure-property prediction using orthogonal Functional Basis neural network
1997 IEEE International Conference on Systems Man and Cybernetics. Computational Cybernetics and Simulation, 1Co-Authors: C.l.p. Chen, Y. Cao, S.r. LeclairAbstract:An important trend in materials research is to predict properties for a new material. Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper proposes a neural-network computing approach to evaluate this issue. With the proposed approach, we are able to predict property features for an unknown compound. In this paper we summarize the prediction attained with the proposed neural network structure-orthogonal Functional Basis neural network (OFBNN). The network, which combines a new Basis selection process and a regularization technique, not only gives us a more computationally tractable method, but better generalization performance. Simulation studies presented demonstrate the performance, behavior, and advantages of the proposed network.
Joshua A. Plumley - One of the best experts on this subject based on the ideXlab platform.
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a comparison of the behavior of Functional Basis set combinations for hydrogen bonding in the water dimer with emphasis on Basis set superposition error
Journal of Computational Chemistry, 2011Co-Authors: Joshua A. Plumley, J. J. DannenbergAbstract:We evaluate the performance of ten Functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 Basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of Functionals and Basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density Functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller Basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest Basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different Functionals. Small Basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller Basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a Functional that predicts a weak interaction with the largest Basis set. As many applications are complex systems and require economical calculations, we suggest the following Functional/Basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP.
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A comparison of the behavior of Functional/Basis set combinations for hydrogen-bonding in the water dimer with emphasis on Basis set superposition error.
Journal of computational chemistry, 2011Co-Authors: Joshua A. Plumley, J. J. DannenbergAbstract:We evaluate the performance of ten Functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 Basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of Functionals and Basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density Functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller Basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest Basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different Functionals. Small Basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller Basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a Functional that predicts a weak interaction with the largest Basis set. As many applications are complex systems and require economical calculations, we suggest the following Functional/Basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP.
C.l.p. Chen - One of the best experts on this subject based on the ideXlab platform.
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ICNN - Orthogonal Functional Basis neural network for Functional approximation
Proceedings of International Conference on Neural Networks (ICNN'97), 1Co-Authors: C.l.p. Chen, Y. Cao, S.r. LeclairAbstract:Subset selection is a well-known technique for generating an efficient and effective neural network structure. The technique has been combined with regularization to improve the generalization performance of a neural network. In this paper, we show an incongruity involving subset selection and regularization. We present an approach to solve this dissonance wherein our subset selection is derived from a combination of Functional Basis. A more efficient training convergence speed is shown using the new Basis which is derived from an 'orthogonal-Functional-Basis' transformation. With this transformation we propose a new orthogonal Functional Basis neural network structure which is not only more computationally tractable but also gives better generalization performance. Simulation studies are presented that demonstrate the performance, behavior, and advantages of the proposed network.
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Material structure-property prediction using orthogonal Functional Basis neural network
1997 IEEE International Conference on Systems Man and Cybernetics. Computational Cybernetics and Simulation, 1Co-Authors: C.l.p. Chen, Y. Cao, S.r. LeclairAbstract:An important trend in materials research is to predict properties for a new material. Often the prediction is motivated by the search for a material with several important materials property features. The selection of the property features is crucial to the plausibility of the prediction. This paper proposes a neural-network computing approach to evaluate this issue. With the proposed approach, we are able to predict property features for an unknown compound. In this paper we summarize the prediction attained with the proposed neural network structure-orthogonal Functional Basis neural network (OFBNN). The network, which combines a new Basis selection process and a regularization technique, not only gives us a more computationally tractable method, but better generalization performance. Simulation studies presented demonstrate the performance, behavior, and advantages of the proposed network.
Mohamed Boutjdir - One of the best experts on this subject based on the ideXlab platform.
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Functional Basis of Sinus Bradycardia in Congenital Heart Block
Circulation research, 2004Co-Authors: Yuankun Yue, Mohamed BoutjdirAbstract:Congenital heart block (CHB) is a conduction abnormality characterized by complete atrioventricular (AV) block. CHB affects fetuses and/or newborn of mothers with autoantibodies reactive with ribonucleoproteins 48-kDa SSB/La, 52-kDa SSA/Ro, and 60-kDa SSA/Ro. We recently established animal models of CHB and reported, for the first time, significant sinus bradycardia preceding AV block. This unexpected observation implies that the spectrum of conduction abnormalities extends beyond the AV node to also affect the SA node. To test this hypothesis, we investigated the Functional Basis of this sinus bradycardia by characterizing the effects of antibodies from mothers with CHB children (positive IgG) on ionic currents that are known to significantly contribute to spontaneous pacing in SA node cells. We recorded L- ( I Ca.L ) and T- ( I Ca.T ) type Ca 2+ , delayed rectifier K + ( I K ), hyperpolarization-activated ( I f ) currents, and action potentials (APs) from young rabbit SA node cells. We demonstrated that positive IgG significantly inhibited both I Ca.T and I Ca.L and induced sinus bradycardia but did not affect I f and I K . Normal IgG from mothers with healthy children did not affect all the currents studied and APs. These results establish that IgG from mothers with CHB children causes substantial inhibition of I Ca.T and I Ca.L , two important pacemaker currents in rabbit SA node cells and point to both I Ca.T and I Ca.L as major players in the ionic mechanism by which maternal antibodies induce sinus bradycardia in CHB. These novel findings have important clinical significance and suggest that sinus bradycardia may be a potential marker in the detection and prevention of CHB. The full text of this article is available online at http://circres.ahajournals.org
