Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction
作者机构：Department of Materials Engineering(MTM)KU LeuvenB-3001 LeuvenBelgium Department of Electrical Engineering(ESAT)KU LeuvenB-3001 LeuvenBelgium Group ScienceEngineering and TechnologyKU Leuven KulakB-8500 KortrijkBelgium
出 版 物：《npj Computational Materials》 (计算材料学（英文）)
年 卷 期：2020年第6卷第1期
页 面：1643-1653页
核心收录：
学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）]
基 金：This work is supported by:(1)European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(INTERDIFFUSION,grant agreement no 714754) 2)Fonds de la Recherche Scientifique-FNRS and the Fonds Wetenschappelijk Onderzoek-Vlaanderen under EOS Project no 30468160(SeLMA 3)European Research Council under the European Union’s Seventh Framework Programme(FP7/2007-2013)/ERC Advanced Grant:BIOTENSORS(no.339804 4)KU Leuven Internal Funds(C16/15/059).(5)KU Leuven Internal Funds(PDM/18/146
主 题：microstructure alloy enable
摘 要：Multicomponent alloys show intricate microstructure evolution,providing materials engineers with a nearly inexhaustible variety of solutions to enhance material *** microstructure evolution simulations are indispensable to exploit these *** simulations,however,require the handling of high-dimensional and prohibitively large data sets of thermodynamic quantities,of which the size grows exponentially with the number of elements in the alloy,making it virtually impossible to handle the effects of four or more *** this paper,we introduce the use of tensor completion for highdimensional data sets in materials science as a general and elegant solution to this *** show that we can obtain an accurate representation of the composition dependence of high-dimensional thermodynamic quantities,and that the decomposed tensor representation can be evaluated very efficiently in microstructure *** realization enables true multicomponent thermodynamic and microstructure modeling for alloy design.
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