Bayesian Error Estimation In Density-functional Theory

Density-Functional Theory (DFT) : three non-technical explanations

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Abstract: The use of k nearest neighbor (k-NN) and Parzen density estimates to obtain estimates of the Bayes error is investigated under limited design set conditions. By drawing analogies between the k-NN and Parzen procedures, new procedures are suggested, and experimental results are given which indicate that.

. generated within the Bayesian error estimation functional framework [J. Wellendorff, K. T. Lundgaard, A. Møgelhøj, V. Petzold, D. D. Landis, J. K. Nørskov, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 85, 235149 (2012)]. The functional is designed to give reasonably accurate density functional theory (DFT) predictions of.

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Kohn–Sham density functional theory (KS-DFT) is an invaluable tool for enhancing our understanding of molecule–surface interactions. DFT simulations have found.

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(Received 9 June 2005; published 15 November 2005). We present a practical scheme for performing error estimates for density-functional theory calculations. The approach, which is based on ideas from Bayesian statistics, involves creating an ensemble of exchange-correlation functionals by comparing with an.

Jun 9, 2005. Abstract: We present a practical scheme for performing error estimates for Density Functional Theory calculations. The approach which is based on ideas from Bayesian statistics involves creating an ensemble of exchange-correlation functionals by comparing with an experimental database of binding.

AA(CAMP and Department of Physics, Technical University of Denmark, DK-2800 Lyngby, Denmark), AB(CAMP and Department of Physics, Technical University of Denmark, DK.

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We present a practical scheme for performing error estimates for density- functional theory calculations. The approach, which is based on ideas from Bayesian statistics, involves creating an ensemble of exchange-correlation functionals by comparing with an experimental database of binding energies for molecules and.

Mortensen JJ(1), Kaasbjerg K, Frederiksen SL, Nørskov JK, Sethna JP, Jacobsen KW. Author information: (1)CAMP and Department of Physics, Technical.

[cond-mat/0506225] Bayesian Error Estimation in Density. –. Bayesian Error Estimation in Density. error estimates for Density Functional Theory calculations. The approach which is based on ideas from Bayesian.

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