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2篇 您的检索式：作者名="Jeffrey A.Hawk"

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		题名	作者	年代	出处	被引量
	1	Machine-learning informed prediction of high-entropy solid solution formation:Beyond the Hume-Rothery rules显示文摘The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using small data sets.Based on an unprecedented large dataset containing 1252 multicomponent alloys,machine-learning methods showed that the formation of solid solutions can be very accurately predicted(93%).The machine-learning results help identify the most important features,such as molar volume,bulk modulus,and melting temperature.	Zongrui Pei Junqi Yin Jeffrey A.Hawk David E.Alman Michael C.Gao	2020	npj Computational Materials2020，，1：	6
	2	Coupling physics in machine learning to predict properties of high-temperatures alloys显示文摘High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales.We propose a workflow that couples highly relevant physics into machine learning(ML)to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength.We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset.Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism.	Jian Peng Yukinori Yamamoto Jeffrey A.Hawk Edgar Lara-Curzio Dongwon Shin	2020	npj Computational Materials2020，，1：	2

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