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1篇 您的检索式：作者名="Raghavendra Selvan"

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		题名	作者	年代	出处	被引量
	1	Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning显示文摘Characterization of material structure with X-ray or neutron scattering using e.g.Pair Distribution Function(PDF)analysis most often rely on refining a structure model against an experimental dataset.However,identifying a suitable model is often a bottleneck.Recently,automated approaches have made it possible to test thousands of models for each dataset,but these methods are computationally expensive and analysing the output,i.e.extracting structural information from the resulting fits in a meaningful way,is challenging.Our Machine Learning based Motif Extractor(ML-MotEx)trains an ML algorithm on thousands of fits,and uses SHAP(SHapley Additive exPlanation)values to identify which model features are important for the fit quality.We use the method for 4 different chemical systems,including disordered nanomaterials and clusters.ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.	Andy S.Anker Emil T.S.Kjær Mikkel Juelsholt Troels Lindahl Christiansen Susanne Linn Skjærvø Mads Ry Vogel Jørgensen Innokenty Kantor Daniel Risskov Sørensen Simon J.L.Billinge Raghavendra Selvan Kirsten M.Ø.Jensen	2022	npj Computational Materials2022，，1：	0

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