Page 137 - Read Online

Chen et al. J Mater Inf 2023;3:10 https://dx.doi.org/10.20517/jmi.2023.06 Page 17 of 19

composites. J Alloys Compd 2017;694:869-76. DOI
107. Panina E, Yurchenko N, Tojibaev A, Mishunin M, Zherebtsov S, Stepanov N. Mechanical properties of (HfCo) (NbMo)
100-x x
refractory high-entropy alloys with a dual-phase bcc-B2 structure. J Alloys Compd 2022;927:167013. DOI
108. Bai J, Wang Z, Zhang M, Qiao J. Effects of tailoring Zn additions on the microstructural evolution and electrical properties in
GaInSnZn high-entropy alloys. Adv Eng Mater 2023. DOI
x
109. Wu M, Wang S, Huang H, Shu D, Sun B. CALPHAD aided eutectic high-entropy alloy design. Mater Lett 2020;262:127175. DOI
110. Yurchenko N, Panina E, Zherebtsov S, Stepanov N. Design and characterization of eutectic refractory high entropy alloys. Materialia
2021;16:101057. DOI
111. Chen H, Mao H, Chen Q. Database development and Calphad calculations for high entropy alloys: challenges, strategies, and tips.
Mater Chem Phys 2018;210:279-90. DOI
112. Zhou Z, Shang Y, Yang Y. A critical review of the machine learning guided design of metallic glasses for superior glass-forming
ability. J Mater Inf 2022;2:2. DOI
113. Qiao L, Ramanujan R, Zhu J. Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent
mechanical properties. Mater Sci Eng A 2022;845:143198. DOI
114. Zhang C, Zhang F, Chen S, Cao W. Computational thermodynamics aided high-entropy alloy design. JOM 2012;64:839-45. DOI
115. Wei Q, Luo G, Zhang J, et al. Designing high entropy alloy-ceramic eutectic composites of MoNbRe TaW(TiC) with high
0.5 x
compressive strength. J Alloys Compd 2020;818:152846. DOI
116. Qu N, Chen Y, Lai Z, Liu Y, Zhu J. The phase selection via machine learning in high entropy alloys. Procedia Manuf 2019;37:299-
305. DOI
117. 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
118. Chang H, Tao Y, Liaw PK, Ren J. Phase prediction and effect of intrinsic residual strain on phase stability in high-entropy alloys with
machine learning. J Alloys Compd 2022;921:166149. DOI
119. Batista GEAPA, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data.
SIGKDD Explor Newsl 2004;6:20-9. DOI
120. Zhu Z, Ma K, Wang Q, Shek C. Compositional dependence of phase formation and mechanical properties in three CoCrFeNi-(Mn/
Al/Cu) high entropy alloys. Intermetallics 2016;79:1-11. DOI
121. Tian Q, Zhang G, Yin K, Cheng W, Wang Y, Huang J. Effect of Ni content on the phase formation, tensile properties and
deformation mechanisms of the Ni-rich AlCoCrFeNi (x = 2, 3, 4) high entropy alloys. Mater Charact 2021;176:111148. DOI
x
122. Wen C, Zhang Y, Wang C, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater
2019;170:109-17. DOI
123. Dewangan SK, Kumar V. Application of artificial neural network for prediction of high temperature oxidation behavior of
AlCrFeMnNiW (x = 0, 0.05, 0.1, 0.5) high entropy alloys. Int J Refract Met Hard Mater 2022;103:105777. DOI
x
124. Roy A, Balasubramanian G. Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys. Comput
Mater Sci 2021;193:110381. DOI
125. Krishna YV, Jaiswal UK, R RM. Machine learning approach to predict new multiphase high entropy alloys. Scr Mater
2021;197:113804. DOI
126. Jaiswal UK, Vamsi Krishna Y, Rahul M, Phanikumar G. Machine learning-enabled identification of new medium to high entropy
alloys with solid solution phases. Comput Mater Sci 2021;197:110623. DOI
127. Islam N, Huang W, Zhuang HL. Machine learning for phase selection in multi-principal element alloys. Comput Mater Sci
2018;150:230-5. DOI
128. Machaka R. Machine learning-based prediction of phases in high-entropy alloys. Comput Mater Sci 2021;188:110244. DOI
129. Elton LRB. Atomic theory for students of metallurgy. Phys Bull 1960;11:309-309. DOI
130. Huang W, Martin P, Zhuang HL. Machine-learning phase prediction of high-entropy alloys. Acta Mater 2019;169:225-36. DOI
131. Chang Y, Jui C, Lee W, Yeh A. Prediction of the composition and hardness of high-entropy alloys by machine learning. JOM
2019;71:3433-42. DOI
132. Bundela AS, Rahul MR. Application of machine learning algorithms with and without principal component analysis for the design of
new multiphase high entropy alloys. Metall Mater Trans A 2022;53:3512-9. DOI
133. Jackson K, Hunt J. Lamellar and rod eutectic growth. dynamics of curved fronts. Elsevier; 1988. pp. 363-76. DOI
134. Zhang Y, Wen C, Wang C, et al. Phase prediction in high entropy alloys with a rational selection of materials descriptors and
machine learning models. Acta Mater 2020;185:528-39. DOI
135. Yan Y, Lu D, Wang K. Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning. Comput
Mater Sci 2021;199:110723. DOI
136. Kaufmann K, Vecchio KS. Searching for high entropy alloys: a machine learning approach. Acta Mater 2020;198:178-222. DOI
137. Xiong J, Shi S, Zhang T. Machine learning of phases and mechanical properties in complex concentrated alloys. J Mater Sci Technol
2021;87:133-42. DOI
138. Bobbili R, Ramakrishna B, Madhu V. Development of machine learning based models for design of high entropy alloys. Mater
Technol 2022;37:2580-7. DOI
139. Mandal P, Choudhury A, Mallick AB, Ghosh M. Phase prediction in high entropy alloys by various machine learning modules using