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90. 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. DOI
91. Purcell TAR, Scheffler M, Carbogno C, Ghiringhelli LM. SISSO++: A C++ implementation of the sure-independence screening and
sparsifying operator approach. J Open Res Softw 2022;7:3960. DOI
92. Sorkin V, Yu ZG, Chen S, Tan TL, Aitken ZH, Zhang YW. A first-principles-based high fidelity, high throughput approach for the
design of high entropy alloys. Sci Rep 2022;12:11894. DOI PubMed PMC
93. Hautier G, Jain A, Ong SP. From the computer to the laboratory: materials discovery and design using first-principles calculations. J
Mater Sci 2012;47:7317-40. DOI
94. Ikeda Y, Grabowski B, Körmann F. Ab initio phase stabilities and mechanical properties of multicomponent alloys: a comprehensive
review for high entropy alloys and compositionally complex alloys. Mater Charact 2019;147:464-511. DOI
95. Kohn W, Sham LJ. Self-consistent equations including exchange and correlation effects. Phys Rev 1965;140:A1133-8. DOI
96. Ceperley DM, Alder BJ. Ground state of the electron gas by a stochastic method. Phys Rev Lett 1980;45:566-9. DOI
97. Perdew JP. Density-functional approximation for the correlation energy of the inhomogeneous electron gas. Phys Rev B Condens
Matter 1986;33:8822-4. DOI
98. Perdew JP, Burke K, Ernzerhof M. Generalized gradient approximation made simple. Phys Rev Lett 1996;77:3865-8. DOI PubMed
99. Perdew JP, Chevary JA, Vosko SH, et al. Atoms, molecules, solids, and surfaces: applications of the generalized gradient
approximation for exchange and correlation. Phys Rev B Condens Matter 1992;46:6671-87. DOI
100. 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;181:124-38. DOI
101. Rittiruam M, Noppakhun J, Setasuban S, et al. High-throughput materials screening algorithm based on first-principles density
functional theory and artificial neural network for high-entropy alloys. Sci Rep 2022;12:16653. DOI PubMed PMC
102. Bellaiche L, Vanderbilt D. Virtual crystal approximation revisited: application to dielectric and piezoelectric properties of
perovskites. Phys Rev B 2000;61:7877-82. DOI
103. Ramer N, Rappe A. Application of a new virtual crystal approach for the study of disordered perovskites. J Phys Chem Solids
2000;61:315-20. DOI
104. Chen L, Hao X, Wang Y, Zhang X, Liu H. First-principles calculation of the effect of Ti content on the structure and properties of
TiVNbMo refractory high-entropy alloy. Mater Res Express 2020;7:106516. DOI
105. Lederer Y, Toher C, Vecchio KS, Curtarolo S. The search for high entropy alloys: a high-throughput ab-initio approach. Acta Mater
2018;159:364-83. DOI
106. Curtarolo S, Setyawan W, Hart GL, et al. AFLOW: an automatic framework for high-throughput materials discovery. Comput Mater
Sci 2012;58:218-26. DOI
107. Sanchez J, Ducastelle F, Gratias D. Generalized cluster description of multicomponent systems. Physica A 1984;128:334-50. DOI
108. de Walle A, Asta M, Ceder G. The alloy theoretic automated toolkit: a user guide. Calphad 2002;26:539-53. DOI
109. Berding MA, Sher A. Electronic quasichemical formalism: application to arsenic deactivation in silicon. Phys Rev B 1998;58:3853-
64. DOI
110. Jiang L, Lu Y, Jiang H, et al. Formation rules of single phase solid solution in high entropy alloys. Mater Sci Technol 2015. DOI
111. Guo S, Ng C, Lu J, Liu CT. Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys. J Appl
Phys 2011;109:103505. DOI
112. Yang S, Liu G, Zhong Y. Revisit the VEC criterion in high entropy alloys (HEAs) with high-throughput ab initio calculations: a case
study with Al-Co-Cr-Fe-Ni system. J Alloys Compd 2022;916:165477. DOI
113. Zhou K & Liu B. Molecular dynamics simulation: fundamentals and applications. Academic Press; 2022. DOI
114. Car R, de Angelis F, Giannozzi P, Marzari N. First-principles molecular dynamics. In: Yip S, editor. Handbook of Materials
Modeling. Dordrecht: Springer; 2005. pp. 59-76. DOI
115. Tang Y, Li D. Nano-tribological behavior of high-entropy alloys CrMnFeCoNi and CrFeCoNi under different conditions: a molecular
dynamics study. Wear 2021;476:203583. DOI
116. Yin S, Zuo Y, Abu-Odeh A, et al. Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of
chemical short-range order. Nat Commun 2021;12:4873. DOI PubMed PMC
117. Fan Y, Wang W, Hao Z, Zhan C. Work hardening mechanism based on molecular dynamics simulation in cutting Ni-Fe-Cr series of
Ni-based alloy. J Alloys Compd 2020;819:153331. DOI
118. Li J, Fang Q, Liu B, Liu Y, Liu Y. Mechanical behaviors of AlCrFeCuNi high-entropy alloys under uniaxial tension via molecular
dynamics simulation. RSC Adv 2016;6:76409-19. DOI
119. Trong DN, Long VC, Ţălu Ş. Effects of number of atoms and doping concentration on the structure, phase transition, and
crystallization process of Fe Ni Coy alloy: a molecular dynamic study. Appl Sci 2022;12:8473. DOI
1-x-y x
120. Xie L, Brault P, Thomann A, Yang X, Zhang Y, Shang G. Molecular dynamics simulation of Al-Co-Cr-Cu-Fe-Ni high entropy alloy
thin film growth. Intermetallics 2016;68:78-86. DOI
121. Pan Z, Fu Y, Wei Y, Yan X, Luo H, Li X. Deformation mechanisms of TRIP-TWIP medium-entropy alloys via molecular dynamics
simulations. Int J Mech Sci 2022;219:107098. DOI
122. Jarlöv A, Ji W, Zhu Z, et al. Molecular dynamics study on the strengthening mechanisms of Cr-Fe-Co-Ni high-entropy alloys based
on the generalized stacking fault energy. J Alloys Compd 2022;905:164137. DOI