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The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety of representations that can be used to convert them into a finite set of symmetric descriptors or features.
Approximation Theory and Methods
M J D Powell · 1981
Earlier work this paper cites.
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Earlier work this paper cites.
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