Local-metrics error-based Shepard interpolation as surrogate for highly non-linear material models in high dimensions-Reference-Cited by-同舟云学术

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Local-metrics error-based Shepard interpolation as surrogate for highly non-linear material models in high dimensions

Published:2017-10-28 Issue:16 Volume:147 Page:164106
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:The Journal of Chemical Physics

Author:

Lorenzi Juan M.¹^ORCID,Stecher Thomas¹^ORCID,Reuter Karsten¹^ORCID,Matera Sebastian²

Affiliation:

1. Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstr. 4, 85747 Garching, Germany

2. Fachbereich für Mathematik und Informatik, Freie Universität Berlin, Otto-von-Simson-Str. 19, D-14195 Berlin, Germany

Funder

Deutsche Forschungsgemeinschaft

Einstein Stiftung Berlin

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/1.4997286

Reference48 articles.

1. Representing potential energy surfaces by high-dimensional neural network potentials

2. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

3. Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression

4. Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

5. Representation of the 6D potential energy surface for a diatomic molecule near a solid surface

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