Page 138 - Read Online
P. 138
Page 18 of 19 Chen et al. J Mater Inf 2023;3:10 https://dx.doi.org/10.20517/jmi.2023.06
thermodynamic and configurational parameters. Met Mater Int 2023;29:38-52. DOI
140. Wang C, Zhong W, Zhao J. Insights on phase formation from thermodynamic calculations and machine learning of 2436
experimentally measured high entropy alloys. J Alloys Compd 2022;915:165173. DOI
141. 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 Des 2021;202:109532. DOI
142. Nassar A, Mullis A. Rapid screening of high-entropy alloys using neural networks and constituent elements. Comput Mater Sci
2021;199:110755. DOI
143. Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials
science. NPJ Comput Mater 2019:5. DOI
144. Pei Z, Yin J, Hawk JA, Alman DE, Gao MC. Machine-learning informed prediction of high-entropy solid solution formation: beyond
the Hume-Rothery rules. NPJ Comput Mater 2020:6. DOI
145. Qu N, Liu Y, Zhang Y, et al. Machine learning guided phase formation prediction of high entropy alloys. Mater Today Commun
2022;32:104146. DOI
146. Bundela AS, Rahul M. Machine learning-enabled framework for the prediction of mechanical properties in new high entropy alloys. J
Alloys Compd 2022;908:164578. DOI
147. Yang C, Ren C, Jia Y, Wang G, Li M, Lu W. A machine learning-based alloy design system to facilitate the rational design of high
entropy alloys with enhanced hardness. Acta Mater 2022;222:117431. DOI
148. Tao Q, Xu P, Li M, Lu W. Machine learning for perovskite materials design and discovery. NPJ Comput Mater 2021:7. DOI
149. Zhang L, Chen H, Tao X, et al. Machine learning reveals the importance of the formation enthalpy and atom-size difference in
forming phases of high entropy alloys. Mater Des 2020;193:108835. DOI
150. Chanda B, Jana PP, Das J. A tool to predict the evolution of phase and Young’s modulus in high entropy alloys using artificial neural
network. Comput Mater Sci 2021;197:110619. DOI
151. Jain R, Dewangan SK, Kumar V, Samal S. Artificial neural network approach for microhardness prediction of eight component
FeCoNiCrMnVAlNb eutectic high entropy alloys. Mater Sci Eng A 2020;797:140059. DOI
152. 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
153. Kumar A, Goel S, Sinha N, Bhardwaj A. A review on unbalanced data classification. In: Uddin MS, Jamwal PK, Bansal JC, editors.
Proceedings of International Joint Conference on Advances in Computational Intelligence. Singapore: Springer Nature; 2022. pp.
197-208. DOI
154. Chawla N V, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res
2002;16:321-57. DOI
155. Tomek I. Tomek link: two modifications of CNN. IEEE Trans Syst Man Cybern 1976;SMC-6:769-772. DOI
156. Lin KB, Weng W, Lai RK, Lu P. Imbalance data classification algorithm based on SVM and clustering function. Proc 9th Int Conf
Comput Sci Educ ICCCSE 2014 2014:544-8. DOI
157. Moreno-Torres JG, Herrera F. A preliminary study on overlapping and data fracture in imbalanced domains by means of genetic
programming-based feature extraction. Proc 2010 10th Int Conf Intell Syst Des Appl ISDA’10 2010:501-6. DOI
158. Bhowan U, Jahnston M, Zhang M. Developing new fitness functions in genetic programming for classification with unbalanced data.
IEEE Trans Syst Man Cybern Part B 2012;42:406-21. DOI
159. Pei Z, Rozman KA, Doğan ÖN, et al. Machine-learning microstructure for inverse material design. Adv Sci 2021;8:e2101207. DOI
PubMed PMC
160. Lee SY, Byeon S, Kim HS, Jin H, Lee S. Deep learning-based phase prediction of high-entropy alloys: optimization, generation, and
explanation. Mater Des 2021;197:109260. DOI
161. Wu X, Zhu Y. Heterogeneous materials: a new class of materials with unprecedented mechanical properties. Mater Res Lett
2017;5:527-32. DOI
162. Zhu Y, Wu X. Perspective on hetero-deformation induced (HDI) hardening and back stress. Mater Res Lett 2019;7:393-8. DOI
163. Sathiyamoorthi P, Kim HS. High-entropy alloys with heterogeneous microstructure: Processing and mechanical properties. Prog
Mater Sci 2022;123:100709. DOI
164. Gwalani B, Gangireddy S, Zheng Y, Soni V, Mishra RS, Banerjee R. Influence of ordered L1(2) precipitation on strain-rate
dependent mechanical behavior in a eutectic high entropy alloy. Sci Rep 2019;9:6371. DOI PubMed PMC
165. Yang Z, Wang Z, Wu Q, et al. Enhancing the mechanical properties of casting eutectic high entropy alloys with Mo addition. Appl
Phys A 2019:125. DOI
166. Lu Y, Gao X, Jiang L, et al. Directly cast bulk eutectic and near-eutectic high entropy alloys with balanced strength and ductility in a
wide temperature range. Acta Mater 2017;124:143-50. DOI
167. Ma L, Wang J, Jin P. Microstructure and mechanical properties variation with Ni content in Al CoCr Fe Ni (x = 1.1, 1.5, 1.8,
0.8 0.6 0.7 x
2.0) eutectic high-entropy alloy system. Mater Res Express 2020;7:016566. DOI
168. Wu Q, Wang Z, Zheng T, et al. A casting eutectic high entropy alloy with superior strength-ductility combination. Mater Lett
2019;253:268-71. DOI
169. Liu Q, Liu X, Fan X, et al. Designing novel AlCoCrNi eutectic high entropy alloys. J Alloys Compd 2022;904:163775. DOI
170. Dong Y, Yao Z, Huang X, et al. Microstructure and mechanical properties of AlCo CrFeNi eutectic high-entropy-alloy system. J
x 3-x