Page 117 - Read Online
P. 117

Page 42 of 45                         Mooraj et al. J Mater Inf 2023;3:4  https://dx.doi.org/10.20517/jmi.2022.41

               123.      Li J, Xie B, Fang Q, Liu B, Liu Y, Liaw PK. High-throughput simulation combined machine learning search for optimum elemental
                    composition in medium entropy alloy. J Mater Sci Technol 2021;68:70-5.  DOI
               124.      Zhang L, Qian K, Huang J, Liu M, Shibuta Y. Molecular dynamics simulation and machine learning of mechanical response in non-
                    equiatomic FeCrNiCoMn high-entropy alloy. J Mater Res Technol 2021;13:2043-54.  DOI
               125.      Morrissey LS, Nakhla S. Considerations when calculating the mechanical properties of single crystals and bulk polycrystals from
                    molecular dynamics simulations. Mol Simul 2020;46:1433-42.  DOI
               126.      Zhang L, Qian K, Schuller BW, Shibuta Y. Prediction on mechanical properties of non-equiatomic high-entropy alloy by atomistic
                    simulation and machine learning. Metals 2021;11:922.  DOI
               127.      Jiang J, Sun W, Luo N. Molecular dynamics study of microscopic deformation mechanism and tensile properties in Al CoCrFeNi
                                                                                                   x
                    amorphous high-entropy alloys. Mater Today Commun 2022;31:103861.  DOI
               128.      Guruvidyathri K, Hari Kumar KC, Yeh JW, Murty BS. Topologically close-packed phase formation in high entropy alloys: a review
                    of calphad and experimental results. JOM 2017;69:2113-24.  DOI
               129.      Gao MC. Design of high-entropy alloys. In: Gao MC, Yeh J, Liaw PK, Zhang Y, editors. High-entropy alloys. Cham: Springer
                    International Publishing; 2016. pp. 369-98.  DOI
               130.      Senkov ON, Miller JD, Miracle DB, Woodward C. Accelerated exploration of multi-principal element alloys with solid solution
                    phases. Nat Commun 2015;6:6529.  DOI  PubMed  PMC
               131.      Klaver TPC, Simonovic D, Sluiter MHF. Brute force composition scanning with a CALPHAD database to find low temperature body
                    centered cubic high entropy alloys. Entropy 2018;20:911.  DOI  PubMed  PMC
               132.      Thurston KV, Gludovatz B, Hohenwarter A, Laplanche G, George EP, Ritchie RO. Effect of temperature on the fatigue-crack growth
                    behavior of the high-entropy alloy CrMnFeCoNi. Intermetallics 2017;88:65-72.  DOI
               133.      Li YJ, Savan A, Ludwig A. Atomic scale understanding of phase stability and decomposition of a nanocrystalline CrMnFeCoNi
                    Cantor alloy. Appl Phys Lett 2021;119:201910.  DOI
               134.      Zeng Z, Xiang M, Zhang D, et al. Mechanical properties of Cantor alloys driven by additional elements: a review. J Mater Res
                    Technol 2021;15:1920-34.  DOI
               135.      Conway PL, Klaver T, Steggo J, Ghassemali E. High entropy alloys towards industrial applications: high-throughput screening and
                    experimental investigation. Mater Sci Eng A 2022;830:142297.  DOI
               136.      Abu-odeh A, Galvan E, Kirk T, et al. Efficient exploration of the high entropy alloy composition-phase space. Acta Mater
                    2018;152:41-57.  DOI
               137.      Zhao DQ, Pan SP, Zhang Y, Liaw PK, Qiao JW. Structure prediction in high-entropy alloys with machine learning. Appl Phys Lett
                    2021;118:231904.  DOI
               138.      Schleder GR, Padilha ACM, Acosta CM, Costa M, Fazzio A. From DFT to machine learning: recent approaches to materials science-
                    a review. J Phys Mater 2019;2:032001.  DOI
               139.      Zhou Z, Zhou Y, He Q, Ding Z, Li F, Yang Y. Machine learning guided appraisal and exploration of phase design for high entropy
                    alloys. NPJ Comput Mater 2019:5.  DOI
               140.      Davydov AV, Kattner UR. Predicting synthesizability. J Phys D Appl Phys 2019;52:013001.  DOI  PubMed  PMC
               141.      Jiang J, Chen P, Qiu J, et al. Microstructural evolution and mechanical properties of Al CoCrFeNi high-entropy alloys under uniaxial
                                                                            x
                    tension: a molecular dynamics simulations study. Mater Today Commun 2021;28:102525.  DOI
               142.      Wang L, Liu W, Zhu B, et al. Influences of strain rate, Al concentration and grain heterogeneity on mechanical behavior of
                    CoNiFeAl Cu  high-entropy alloys: a molecular dynamics simulation. J Mater Res Technol 2021;14:2071-84.  DOI
                           x  1-x
               143.      Leong Z, Tan TL. Robust cluster expansion of multicomponent systems using structured sparsity. Phys Rev B 2019:100.  DOI
               144.      Leong Z, Ramamurty U, Tan TL. Microstructural and compositional design principles for Mo-V-Nb-Ti-Zr multi-principal element
                    alloys: a high-throughput first-principles study. Acta Mater 2021;213:116958.  DOI
               145.      Fernández-caballero A, Wróbel JS, Mummery PM, Nguyen-manh D. Short-range order in high entropy alloys: theoretical
                    formulation and application to Mo-Nb-Ta-V-W system. J Phase Equilib Diffus 2017;38:391-403.  DOI
               146.      Fontaine D. The number of independent pair-correlation functions in multicomponent systems. J Appl Crystallogr 1971;4:15-9.  DOI
               147.      Kattner UR. The calphad method and its role in material and process development. Tecnol Metal Mater Min 2016;13:3-15.  DOI
                    PubMed  PMC
               148.      Zeng Y, Man M, Bai K, Zhang Y. Revealing high-fidelity phase selection rules for high entropy alloys: a combined CALPHAD and
                    machine learning study. Mater Design 2021;202:109532.  DOI
               149.      Wu D, Tian Y, Zhang L, et al. Optimal design of high-strength Ti-Al-V-Zr alloys through a combinatorial approach. Materials
                    2018;11:1603.  DOI  PubMed  PMC
               150.      Gumbmann E, De Geuser F, Deschamps A, Lefebvre W, Robaut F, Sigli C. A combinatorial approach for studying the effect of Mg
                    concentration on precipitation in an Al-Cu-Li alloy. Scr Mater 2016;110:44-7.  DOI
               151.      Li Y, Jensen KE, Liu Y, et al. Combinatorial strategies for synthesis and characterization of alloy microstructures over large
                    compositional ranges. ACS Comb Sci 2016;18:630-7.  DOI  PubMed
               152.      Tang M, Pistorius PC, Narra S, Beuth JL. Rapid solidification: selective laser melting of AlSi Mg. JOM 2016;68:960-6.  DOI
                                                                                10
               153.      Aboutaleb AM, Mahtabi MJ, Tschopp MA, Bian L. Multi-objective accelerated process optimization of mechanical properties in
                    laser-based additive manufacturing: case study on Selective Laser Melting (SLM) Ti-6Al-4V. J Manuf Process 2019;38:432-44.  DOI
               154.      Jung HY, Peter NJ, Gärtner E, Dehm G, Uhlenwinkel V, Jägle EA. Bulk nanostructured AlCoCrFeMnNi chemically complex alloy
   112   113   114   115   116   117   118   119   120   121   122