Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials. Onat, B., Ortner, C., & Kermode, J. R. The Journal of Chemical Physics, American Institute of Physics, October, 2020.
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials [link]Paper  abstract   bibtex   
Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of these representations and then investigate (i) the sensitivity to perturbations and (ii) the effective dimensionality of a variety of atomic environment representations and over a range of material datasets. Representations investigated include atom centered symmetry functions, Chebyshev Polynomial Symmetry Functions (CHSF), smooth overlap of atomic positions, many-body tensor representation, and atomic cluster expansion. In area (i), we show that none of the atomic environment representations are linearly stable under tangential perturbations and that for CHSF, there are instabilities for particular choices of perturbation, which we show can be removed with a slight redefinition of the representation. In area (ii), we find that most representations can be compressed significantly without loss of precision and, further, that selecting optimal subsets of a representation method improves the accuracy of regression models built for a given dataset.
@article{wrap143090,
          volume = {153},
          number = {14},
           month = {October},
          author = {Berk Onat and Christoph Ortner and James R. Kermode},
           title = {Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials},
       publisher = {American Institute of Physics},
         journal = {The Journal of Chemical Physics},
            year = {2020},
             url = {http://wrap.warwick.ac.uk/143090/},
        abstract = {Faithfully representing chemical environments is essential for describing materials and molecules with machine learning approaches. Here, we present a systematic classification of these representations and then investigate (i) the sensitivity to perturbations and (ii) the effective dimensionality of a variety of atomic environment representations and over a range of material datasets. Representations investigated include atom centered symmetry functions, Chebyshev Polynomial Symmetry Functions (CHSF), smooth overlap of atomic positions, many-body tensor representation, and atomic cluster expansion. In area (i), we show that none of the atomic environment representations are linearly stable under tangential perturbations and that for CHSF, there are instabilities for particular choices of perturbation, which we show can be removed with a slight redefinition of the representation. In area (ii), we find that most representations can be compressed significantly without loss of precision and, further, that selecting optimal subsets of a representation method improves the accuracy of regression models built for a given dataset.
}
}
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