摘要
High Entropy Alloys(HEAs)are composed of more than one principal element and constitute a major paradigm in metals research.The HEA space is vast and an exhaustive exploration is improbable.Therefore,a thorough estimation of the phases present in the HEA is of paramount importance for alloy design.Machine Learning presents a feasible and non-expensive method for predicting possible new HEAs on-the-fly.A deep neural network(DNN)model for the elemental system of:Mn,Ni,Fe,Al,Cr,Nb,and Co is developed using a dataset generated by high-throughput computational thermodynamic calculations using Thermo-Calc.The features list used for the neural network is developed based on literature and freely available databases.A feature significance analysis matches the reported HEAs phase constitution trends on elemental properties and further expands it by providing so far-overlooked features.The final regressor has a coefficient of determination(r^(2))greater than 0.96 for identifying the most recurrent phases and the functionality is tested by running optimization tasks that simulate those required in alloy design.The DNN developed constitutes an example of an emulator that can be used in fast,real-time materials discovery/design tasks.
基金
R.A.and G.V.acknowledge the support of QNRF under Project No.NPRP11S-1203-170056
Support from NSF through Grants No.1545403,1905325
2119103 is acknowledged.High-throughput CALPHAD calculations were carried out in part at the Texas A&M High-Performance Research Computing(HPRC)Facility.R.G
acknowledges this material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No.(DGE:1746932)
Any opinions,findings,conclusions or recommendations expressed in this material are those of the authors(s)and do not necessarily reflect the views of the National Science Foundation.S.C
was supported in part by the Advanced Research Projects Agency-Energy(ARPA-E),U.S.Department of Energy,under award number DE-AR0001356.
