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1篇 您的检索式：作者名="Matthew R.Carbone"

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		题名	作者	年代	出处	被引量
	1	Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships显示文摘X-ray absorption spectroscopy(XAS)produces a wealth of information about the local structure of materials,but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure.Recently,researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra.However,machine learning models are often difficult to interpret,making it challenging to determine when they are valid and whether they are consistent with physical theories.In this work,we present three main advances to the data-driven analysis of XAS spectra:we demonstrate the efficacy of random forests in solving two new property determination tasks(predicting Bader charge and mean nearest neighbor distance),we address how choices in data representation affect model interpretability and accuracy,and we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties.	Steven B.Torrisi Matthew R.Carbone Brian A.Rohr Joseph H.Montoya Yang Ha Junko Yano Santosh K.Suram Linda Hung	2020	npj Computational Materials2020，，1：	3

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