Fig. 2: Pseudo-code describing the four steps of ML-MotEx. | npj Computational Materials

Fig. 2: Pseudo-code describing the four steps of ML-MotEx.

From: Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Fig. 2

A starting model, fitting script, and dataset are given as input. Firstly, a catalogue of candidate structure motifs is generated (step 1), which are fitted to the dataset (step 2). The output from step 1 and 2 is then given to an ML algorithm, which learns to predict goodness-of-fit (Rwp) values based on the structure motif (step 3). Lastly, SHAP values are calculated for each feature (step 4) which can be converted to atom contribution values.

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