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Found 2 papers, 1 papers with code
Optimal radial basis for density-based atomic representations
For each training dataset and number of basis functions, one can determine a unique basis that is optimal in this sense, and can be computed at no additional cost with respect to the primitive basis by approximating it with splines.
The role of feature space in atomistic learning
In this work we introduce a framework to compare different sets of descriptors, and different ways of transforming them by means of metrics and kernels, in terms of the structure of the feature space that they induce.
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