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1篇 您的检索式：作者名="Kenneth S.Vecchio"

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		题名	作者	年代	出处	被引量
	1	Discovery of high-entropy ceramics via machine learning显示文摘Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications,predicting their formation remains a hindrance for rational discovery of new systems.Experimental approaches are based on physical intuition and/or expensive trial and error strategies.Most computational methods rely on the availability of sufficient experimental data and computational power.Machine learning(ML)applied to materials science can accelerate development and reduce costs.In this study,we propose an ML method,leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability(i.e.,entropy-forming ability)of disordered metal carbides.	Kevin Kaufmann Daniel Maryanovsky William M.Mellor Chaoyi Zhu Alexander S.Rosengarten Tyler J.Harrington Corey Oses Cormac Toher Stefano Curtarolo Kenneth S.Vecchio	2020	npj Computational Materials2020，，1：	5

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