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1篇 您的检索式：作者名="Lars Banko"

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		题名	作者	年代	出处	被引量
	1	Deep learning for visualization and novelty detection in large X-ray diffraction datasets显示文摘We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,such as the structural similarity of textured diffraction patterns.While other artificial intelligence(AI)agents are effective at classifying XRD data into known phases,a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know:it can rapidly identify data outside the distribution it was trained on,such as novel phases and mixtures.These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both‘on-the-fly’and during post hoc analysis.	Lars Banko Phillip M.Maffettone Dennis Naujoks Daniel Olds Alfred Ludwig	2021	npj Computational Materials2021，，1：	2

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