References
- Miracle DB, Senkov ON. A critical review of high entropy alloys and related concepts. Acta Mater. 2017;122:448–511.
- Li W, Liu P, Liao PK. Microstructures and properties of high-entropy alloy films and coatings: a review. Mater Res Lett. 2018;6:199–229.
- Lai WJ, Vogel F, Zhao XY, et al. Design of BCC refractory multi-principal element alloys with superior mechanical properties. Mater Res Lett. 2022;10:133–140.
- Ding Q, Zhang Y, Chen X, et al. Tuning element distribution, structure and properties by composition in high-entropy alloys. Nature. 2019;574:223–227.
- Huang J, Li WP, He JY, et al. Dual heterogeneous structure facilitating an excellent strength-ductility combination in an additively manufactured multi-principal-element alloy. Mater Res Lett. 2022;10:575–584.
- Yao Y, Dong A, Brozena A, et al. High-entropy nanoparticles: synthesis-structure-property relationships and data-driven discovery. Science. 2022;376:eabn3103.
- Pan Q, Zhang L, Feng R, et al. Gradient cell-structured high-entropy alloy with exceptional strength and ductility. Science. 2021;374:984–989.
- Hart GLW, Mueller T, Toher C, et al. Machine learning for alloys. Nat Rev Mater. 2021;6:730–755.
- Kaufmann K, Vecchio KS. Searching for high entropy alloys: a machine learning approach. Acta Mater. 2020;198:178–222.
- Zhou Q, Lu S, Wu Y, et al. Property-oriented material design based on a data-driven machine learning technique. J Phys Chem Lett. 2020;11:3920–3927.
- Wen C, Zhang Y, Wang C, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019;170:109–117.
- de Jong M, Chen W, Notestine R, et al. Statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci Rep. 2016;6:1–11.
- Kim G, Diao H, Lee C, et al. First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation. Acta Mater. 2019;181:124–138.
- Revi V, Kasodariya S, Talapatra A, et al. Machine learning elastic constants of multi-component alloys. Comput Mater Sci. 2021;198:110671.
- Vazquez G, Singh P, Sauceda D, et al. Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys. Acta Mater. 2022;232:117924.
- Hayashi G, Suzuki K, Terai T, et al. Prediction model of elastic constants of BCC high-entropy alloys based on first-principles calculations and machine learning techniques. Sci Technol Adv Mat Methods. 2022;2:381–391.
- Liu S, Lee K, Balachandran PV. Integrating machine learning with mechanistic models for predicting the yield strength of high entropy alloys. J Appl Phys. 2022;132:105105.
- Gao Y, Bai S, Chong K, et al. Machine learning accelerated design of non-equiatomic refractory high entropy alloys based on first principles calculation. Vacuum. 2023;207:111608.
- Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 2013;1:011002.
- Wen D, Chang CH, Matsunaga S, et al. Structure and tensile properties of Mx(MnFeCoNi)100-x solid solution strengthened high entropy alloys. Materialia. 2020;9:100539.
- Zunger A, Wei SH, Ferreira LG, et al. Special quasirandom structures. Phys Rev Lett. 1990;65:353–356.
- Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B. 1996;54:11169–11186.
- Friedman JH. Stochastic gradient boosting. Comput Stat Data An. 2002;38:367–378.
- Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–2830.
- Lookman T, Balachandran PV, Xue D, et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5:1–17.
- Pugh SF. XCII. Relations between the elastic moduli and the plastic properties of polycrystalline pure metals. Philo Mag. 1954;45:823–843.
- Sohn S, Liu Y, Liu J, et al. Noble metal high entropy alloys. Scripta Mater. 2017;126:29–32.
- Thiel F, Geissler G, Nielsch K, et al. Origins of strength and plasticity in the precious metal based high-entropy alloy AuCuNiPdPt. Acta Mater. 2020;185:400–411.
- Varvenne C, Curtin WA. Predicting yield strengths of noble metal high entropy alloys. Scripta Mater. 2018;142:92–95.
- Varvenne C, Luque A, Curtin WA. Theory of strengthening in fcc high entropy alloys. Acta Mater. 2016;118:164–176.
- Varvenne C, Leyson GPM, Ghazisaeidi M, et al. Solute strengthening in random alloys. Acta Mater. 2017;124:660–683.
- Yin B, Curtin WA. First-principles-based prediction of yield strength in the RhIrPdPtNiCu high-entropy alloy. npj Comput Mater. 2019;5:14.
- Otto F, Dlouhý A, Somsen C, et al. The influences of temperature and microstructure on the tensile properties of a CoCrFeMnNi high-entropy alloy. Acta Mater. 2013;61:5743–5755.
- Gludovatz B, Hohenwarter A, Catoor D, et al. A fracture-resistant high-entropy alloy for cryogenic applications. Science. 2014;345:1153–1158.
- Laplanche G, Kostka A, Horst O, et al. Microstructure evolution and critical stress for twinning in the CrMnFeCoNi high-entropy alloy. Acta Mater. 2016;118:152–163.