Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
2018 | Zeitschriftenartikel. Eine Publikation mit Affiliation zur Georg-August-Universität Göttingen.
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Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Nguyen, T. T.; Székely, E.; Imbalzano, G.; Behler, J. ; Csányi, G.; Ceriotti, M. & Götz, A. W. u.a. (2018)
The Journal of Chemical Physics, 148(24) pp. 241725. DOI: https://doi.org/10.1063/1.5024577
