Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
2018 | journal article. A publication with affiliation to the University of Göttingen.
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Nguyen, T. T., Székely, E., Imbalzano, G., Behler, J., Csányi, G., Ceriotti, M., Götz, A. W. ... Paesani, F. (2018). Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. The Journal of Chemical Physics, 148(24), 241725. doi: https://doi.org/10.1063/1.5024577
