Accelerated computational design of BCC refractory high entropy alloys using metaheuristics, CALPHAD, and artificial neural networks
Refractory high-entropy alloys are a novel class of materials for diverse fields. Their design has been enhanced by several empirical models, thermodynamic calculation methods, and artificial intelligence techniques, allowing continuous advancements in the search for specific properties. In this stu...
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| Published in: | Materials today communications Vol. 47; p. 113102 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.07.2025
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| ISSN: | 2352-4928, 2352-4928 |
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| Abstract | Refractory high-entropy alloys are a novel class of materials for diverse fields. Their design has been enhanced by several empirical models, thermodynamic calculation methods, and artificial intelligence techniques, allowing continuous advancements in the search for specific properties. In this study, RHEA was designed to maximize the stabilization temperature range of a high temperature BCC single-phase (high-temperature single-phase, HTSP) while maintaining the mixing entropy (∆Smix) as high as possible, comparable to that of an equimolar composition. Metaheuristic algorithms (particle swarm optimization, genetic algorithms and artificial bee colony), thermodynamic calculations, and artificial neural networks, with a global fitting > 0.99 were employed to determine the optimal chemical composition from over 20000 candidates. Theoretically, the TiNbZrTaMo, TiNbZrTaMoV, TiNbZrTaMoHf, and TiNbZrTaMoVHf alloy families were investigated. From the theoretically obtained results, the Ti17.5Nb17.5Zr13.5Ta15.5Mo17.5V18.5 alloy was chosen in order to prove that the predictive expectations can be reproduced experimentally. The alloy composition was synthesized using an arc-melting furnace under an inert atmosphere and characterized via X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). Additionally, mechanical properties were evaluated through the Vickers microhardness test. The XRD results showed the formation of a single BCC phase, confirming the theoretical predictions. Elemental segregation was also observed in the microstructure, with a remarkable concentration of Zr and Nb in the interdendritic region. Furthermore, the measured Vickers microhardness value was 480 ± 5.2 HV, being higher than previously studied HEAs, highlighting the effect of incorporating a sixth alloying element. These findings demonstrate the feasibility of using thermodynamic calculations and artificial intelligence for the materials design.
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| AbstractList | Refractory high-entropy alloys are a novel class of materials for diverse fields. Their design has been enhanced by several empirical models, thermodynamic calculation methods, and artificial intelligence techniques, allowing continuous advancements in the search for specific properties. In this study, RHEA was designed to maximize the stabilization temperature range of a high temperature BCC single-phase (high-temperature single-phase, HTSP) while maintaining the mixing entropy (∆Smix) as high as possible, comparable to that of an equimolar composition. Metaheuristic algorithms (particle swarm optimization, genetic algorithms and artificial bee colony), thermodynamic calculations, and artificial neural networks, with a global fitting > 0.99 were employed to determine the optimal chemical composition from over 20000 candidates. Theoretically, the TiNbZrTaMo, TiNbZrTaMoV, TiNbZrTaMoHf, and TiNbZrTaMoVHf alloy families were investigated. From the theoretically obtained results, the Ti17.5Nb17.5Zr13.5Ta15.5Mo17.5V18.5 alloy was chosen in order to prove that the predictive expectations can be reproduced experimentally. The alloy composition was synthesized using an arc-melting furnace under an inert atmosphere and characterized via X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). Additionally, mechanical properties were evaluated through the Vickers microhardness test. The XRD results showed the formation of a single BCC phase, confirming the theoretical predictions. Elemental segregation was also observed in the microstructure, with a remarkable concentration of Zr and Nb in the interdendritic region. Furthermore, the measured Vickers microhardness value was 480 ± 5.2 HV, being higher than previously studied HEAs, highlighting the effect of incorporating a sixth alloying element. These findings demonstrate the feasibility of using thermodynamic calculations and artificial intelligence for the materials design.
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| ArticleNumber | 113102 |
| Author | Román-Sedano, A. Monzamodeth Espinosa-Rangel, S. González, G. Figueroa, I.A. Aranda, V. Villalobos, J. Hernández-Mecinas, E. |
| Author_xml | – sequence: 1 givenname: A. Monzamodeth surname: Román-Sedano fullname: Román-Sedano, A. Monzamodeth organization: Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad de México CP 04510, Mexico – sequence: 2 givenname: V. orcidid: 0000-0003-2376-6124 surname: Aranda fullname: Aranda, V. organization: Centro de Ingeniería de Superficies y Acabados (CENISA), Facultad de Ingeniería, División de Ingeniería Mecánica e Industrial, Universidad Nacional Autónoma de México, C.P. 04510, Mexico – sequence: 3 givenname: E. orcidid: 0000-0001-8827-7386 surname: Hernández-Mecinas fullname: Hernández-Mecinas, E. organization: Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad de México CP 04510, Mexico – sequence: 4 givenname: S. surname: Espinosa-Rangel fullname: Espinosa-Rangel, S. organization: Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad de México CP 04510, Mexico – sequence: 5 givenname: J. orcidid: 0009-0007-0472-198X surname: Villalobos fullname: Villalobos, J. organization: Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad de México CP 04510, Mexico – sequence: 6 givenname: I.A. orcidid: 0000-0001-9699-0261 surname: Figueroa fullname: Figueroa, I.A. email: iafigueroa@unam.mx organization: Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad de México CP 04510, Mexico – sequence: 7 givenname: G. surname: González fullname: González, G. email: joseggr@unam.mx organization: Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Ciudad de México CP 04510, Mexico |
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| Cites_doi | 10.1016/j.commatsci.2021.110381 10.1016/j.jallcom.2024.174408 10.1016/j.msea.2021.141098 10.1016/j.jallcom.2024.173605 10.1016/j.compbiomed.2024.108809 10.1016/j.intermet.2018.04.023 10.3390/ma16206622 10.1016/j.mtcomm.2024.109607 10.1016/j.jallcom.2023.170431 10.1016/j.eswa.2020.114312 10.1016/j.jallcom.2023.173279 10.1016/j.commatsci.2021.110723 10.1016/j.matdes.2022.111239 10.1016/j.jallcom.2016.11.188 10.1016/j.commatsci.2023.112612 10.1016/j.mser.2023.100746 10.1016/j.jallcom.2016.09.189 10.1016/j.commatsci.2024.112917 10.1016/j.jallcom.2023.170758 10.1103/PhysRevMaterials.3.095005 10.1016/j.jallcom.2024.178274 10.1016/j.intermet.2010.06.003 10.1007/s11837-019-03704-4 10.1007/s11661-015-3105-z 10.1016/j.jmrt.2025.01.150 10.3390/e20120967 10.1016/j.jallcom.2019.06.387 10.1063/5.0065303 10.1016/j.mattod.2015.11.026 10.1016/j.jmst.2022.08.046 10.1016/j.intermet.2023.108080 |
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| Keywords | Refractory high entropy alloys Mechanical properties Metaheuristics algorithms Microstructure Artificial neural networks Calphad method |
| Language | English |
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| References | Yao, Qiao, Hawk, Zhou, Chen, Gao (bib33) 2017 Román-Sedano, Campillo, Villalobos, Castillo, Flores (bib27) 2023 Yan, Lu, Wang (bib18) 2023 Yao, Liu, Gao, Jiang, Li, Liu, Zhang, Fan (bib30) 2018 Zhou, Cheng, Chen, Liang (bib3) 2022 Yan, Lu, Wang (bib28) 2021 Chen, Tong, Tseng, Yeh, Poplawsky, Wen, Gao, Kim, Chen, Ren, Feng, Li, Liaw (bib32) 2019 Roy, Balasubramanian (bib6) 2021 Hosseini, Vaghefi, Lee, Prorok, Mirkoohi (bib12) 2024 Guo, Hou, Pan, Pei, Song, Liaw, Zhao (bib16) 2023 Takeuchi, Inoue (bib24) 2010 Xiong, Guo, Zhan, Liu, Cao (bib5) 2023 Dangwal, Edalati (bib36) 2025 Iroc, Tukac, Tanrisevdi, El-Atwani, Tunes, Kalay, Aydogan (bib40) 2022 Sun, Wang, Mo, Wang, Liang, Shen (bib43) 2024 Kalali, Antharam, Hasan, Karthik, Phani, Bhanu Sankara Rao, Rajulapati (bib34) 2021 Aranda, Figueroa, Amigó, González-Ojeda, Lozada, Vidilli, Otani, Gonzalez (bib37) 2023 Zhang, Xie, Guo, Ding, Poh, Sha (bib10) 2023 Zhang, Chen, Tao, Cai, Liu, Ouyang, Peng, Du (bib23) 2020 Yao, Qiao, Hawk, Zhou, Chen, Gao (bib41) 2017 Raturi, Aditya C, Gurao, Biswas (bib22) 2019 Sun, Lu, Liu, Du, Xie, Lv, Song, Wu, Wang, Jiang, Lu (bib19) 2021 Melnick, Soolshenko (bib8) 2017 Wu, Tsai, Kuo, Tsai (bib13) 2018 Gao, Zhang, Yang, Guo (bib39) 2016 Naveen, Umre, Chakraborty, Rahul, Samal, Tewari (bib29) 2024 Aranda, Klyatskina, Barba-Pingarrón, Figueroa, Amigó, Gonzalez (bib42) 2025 Hu, Tan, Knibbe, Xu, Jiang, Wang, Li, Zhang (bib11) 2023 Lin, Yao, Yao, Liu, Liu, Zhang, Qin, Wu (bib2) 2023 Gou, LIU (bib4) 2011 Catal, Bedir, Yilmaz, Swider, Lee, El-Atwani, Maier, Ozdemir, Canadinc (bib21) 2024 González-Guillén, Romero-Resendiz, Naeem, Vidilli, Otani, Klyatskina, Gonzalez, Amigó (bib38) 2024 Bencze, Hasemann, Sergeev, Motalov, Müller, Krüger (bib9) 2024 Luíza da Costa, Dias de Lima, Barbosa (bib26) 2021 Xu, Song, Wang, Li, Pan, Hou, Zhao (bib17) 2025 Kumar, Linda, Shadangi, Jindal (bib44) 2024 Kumar Dewangan, Nagarjuna, Jain, Kumawat, Kumar, Sharma, Ahn (bib15) 2023 Li, Guo (bib14) 2019 Ye, Wang, Lu, Liu, Yang (bib1) 2016 Iroc, Tukac, Tanrisevdi, El-Atwani, Tunes, Kalay, Aydogan (bib31) 2022 Avula, Chavan, Mukherjee, Roy (bib35) 2024 Gao, Carney, Dogan, Jablonksi, Hawk, Alman (bib7) 2015 Chang, Jui, Lee, Yeh (bib20) 2019 Alkadri, Del Maestro, Driscoll (bib25) 2024 Kalali (10.1016/j.mtcomm.2025.113102_bib34) 2021 Gao (10.1016/j.mtcomm.2025.113102_bib39) 2016 Chang (10.1016/j.mtcomm.2025.113102_bib20) 2019 Bencze (10.1016/j.mtcomm.2025.113102_bib9) 2024 Wu (10.1016/j.mtcomm.2025.113102_bib13) 2018 Iroc (10.1016/j.mtcomm.2025.113102_bib31) 2022 Xiong (10.1016/j.mtcomm.2025.113102_bib5) 2023 Zhang (10.1016/j.mtcomm.2025.113102_bib10) 2023 Xu (10.1016/j.mtcomm.2025.113102_bib17) 2025 Yan (10.1016/j.mtcomm.2025.113102_bib18) 2023 Catal (10.1016/j.mtcomm.2025.113102_bib21) 2024 Yan (10.1016/j.mtcomm.2025.113102_bib28) 2021 Kumar (10.1016/j.mtcomm.2025.113102_bib44) 2024 Sun (10.1016/j.mtcomm.2025.113102_bib19) 2021 Zhang (10.1016/j.mtcomm.2025.113102_bib23) 2020 Luíza da Costa (10.1016/j.mtcomm.2025.113102_bib26) 2021 Dangwal (10.1016/j.mtcomm.2025.113102_bib36) 2025 Alkadri (10.1016/j.mtcomm.2025.113102_bib25) 2024 Guo (10.1016/j.mtcomm.2025.113102_bib16) 2023 Lin (10.1016/j.mtcomm.2025.113102_bib2) 2023 Takeuchi (10.1016/j.mtcomm.2025.113102_bib24) 2010 Avula (10.1016/j.mtcomm.2025.113102_bib35) 2024 Iroc (10.1016/j.mtcomm.2025.113102_bib40) 2022 González-Guillén (10.1016/j.mtcomm.2025.113102_bib38) 2024 Hu (10.1016/j.mtcomm.2025.113102_bib11) 2023 Yao (10.1016/j.mtcomm.2025.113102_bib30) 2018 Yao (10.1016/j.mtcomm.2025.113102_bib33) 2017 Zhou (10.1016/j.mtcomm.2025.113102_bib3) 2022 Hosseini (10.1016/j.mtcomm.2025.113102_bib12) 2024 Román-Sedano (10.1016/j.mtcomm.2025.113102_bib27) 2023 Ye (10.1016/j.mtcomm.2025.113102_bib1) 2016 Naveen (10.1016/j.mtcomm.2025.113102_bib29) 2024 Chen (10.1016/j.mtcomm.2025.113102_bib32) 2019 Yao (10.1016/j.mtcomm.2025.113102_bib41) 2017 Aranda (10.1016/j.mtcomm.2025.113102_bib42) 2025 Li (10.1016/j.mtcomm.2025.113102_bib14) 2019 Gao (10.1016/j.mtcomm.2025.113102_bib7) 2015 Melnick (10.1016/j.mtcomm.2025.113102_bib8) 2017 Kumar Dewangan (10.1016/j.mtcomm.2025.113102_bib15) 2023 Gou (10.1016/j.mtcomm.2025.113102_bib4) 2011 Aranda (10.1016/j.mtcomm.2025.113102_bib37) 2023 Raturi (10.1016/j.mtcomm.2025.113102_bib22) 2019 Sun (10.1016/j.mtcomm.2025.113102_bib43) 2024 Roy (10.1016/j.mtcomm.2025.113102_bib6) 2021 |
| References_xml | – year: 2023 ident: bib2 article-title: Construction of FeCrVTiMox high-entropy alloys with enhanced mechanical properties based on electronegativity difference regulation strategy publication-title: J. Alloy. Compd. – year: 2022 ident: bib40 article-title: Design of oxygen-doped TiZrHfNbTa refractory high entropy alloys with enhanced strength and ductility publication-title: Mater. Des. – year: 2015 ident: bib7 article-title: Design of refractory high-entropy alloys publication-title: JOM – year: 2023 ident: bib16 article-title: Hardening-softening of Al0.3CoCrFeNi high-entropy alloy under nanoindentation publication-title: Mater. Des. – year: 2023 ident: bib27 article-title: Hydrogen diffusion in nickel superalloys: electrochemical permeation study and computational AI predictive modeling publication-title: Materials – year: 2019 ident: bib14 article-title: Machine-learning model for predicting phase formations of high-entropy alloys publication-title: Phys. Rev. Mater. – year: 2017 ident: bib8 article-title: Thermodynamic design of high-entropy refractory alloys publication-title: J. Alloy. Compd. – year: 2019 ident: bib32 article-title: Phase transformations of HfNbTaTiZr high-entropy alloy at intermediate temperatures publication-title: Scr. Mater. – year: 2016 ident: bib39 article-title: Senary refractory high-entropy alloy HfNbTaTiVZr publication-title: Metall. Mater. Trans. A – year: 2024 ident: bib44 article-title: Influence of micro-segregation on the microstructure, and microhardness of MoNbTaxTi(1-x)W refractory high entropy alloys: experimental and DFT approach publication-title: Intermetallics – year: 2021 ident: bib26 article-title: Evaluation of feature selection methods based on artificial neural network weights publication-title: Expert Syst. Appl. – year: 2024 ident: bib9 article-title: Thermodynamic properties of refractory Mo-Nb-V-Ti high entropy alloys (HEAs) publication-title: J. Alloy. Compd. – year: 2023 ident: bib37 article-title: Effect of Mo on high entropy Ti-Nb-Zr-Ta alloy: phase equilibria, microstructure and mechanical properties publication-title: J. Alloy. Compd. – year: 2024 ident: bib43 article-title: Promoted high-temperature strength and room-temperature plasticity synergy by tuning dendrite segregation in NbMoTaW refractory high-entropy alloy publication-title: Int. J. Refract. Met. Hard Mater. – year: 2025 ident: bib36 article-title: Developing a single-phase and nanograined refractory high-entropy alloy ZrHfNbTaW with ultrahigh hardness by phase transformation via high-pressure torsion publication-title: J. Alloy. Compd. – year: 2021 ident: bib6 article-title: Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys publication-title: Comput. Mater. Sci. – year: 2019 ident: bib20 article-title: Prediction of the composition and hardness of high-entropy alloys by machine learning publication-title: JOM – year: 2016 ident: bib1 article-title: High-entropy alloy: challenges and prospects publication-title: Mater. Today – year: 2021 ident: bib28 article-title: Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning publication-title: Comput. Mater. Sci. – year: 2021 ident: bib34 article-title: On the origins of ultra-high hardness and strain gradient plasticity in multi-phase nanocrystalline MoNbTaTiW based refractory high-entropy alloy publication-title: Materials Science Engineering A – year: 2024 ident: bib35 article-title: Phase stability and mechanical properties of Ta enriched TiTaNbZrMo refractory high entropy alloys publication-title: J. Alloy. Compd. – year: 2023 ident: bib5 article-title: Refractory high-entropy alloys: a focused review of preparation methods and properties publication-title: J. Mater. Sci. Technol. – year: 2025 ident: bib42 article-title: Nanoindentation and orientation imaging analysis of high entropy Ti–Nb–Zr–Ta–Mo alloys exhibiting dendrite microstructure publication-title: J. Mater. Res. Technol. – year: 2017 ident: bib41 article-title: Mechanical properties of refractory high-entropy alloys: experiments and modeling publication-title: J. Alloy. Compd. – year: 2023 ident: bib15 article-title: Review on applications of artificial neural networks to develop high entropy alloys: a state-of-the-art technique publication-title: Mater. Today Commun. – year: 2017 ident: bib33 article-title: Mechanical properties of refractory high-entropy alloys: experiments and modelling publication-title: J. Alloy. Compd. – year: 2019 ident: bib22 article-title: ICME approach to explore equiatomic and non equiatomic single phase BCC refractory high entropy alloys publication-title: J. Alloy. Compd. – year: 2023 ident: bib11 article-title: Recent applications of machine learning in alloy design: a review publication-title: Materials Science Engineering R Reports – year: 2023 ident: bib10 article-title: Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach publication-title: J. Alloy. Compd. – year: 2024 ident: bib12 article-title: Machine learning-enabled prediction and optimization of hardness for Nb-Ti-V-Zr refractory high entropy alloy publication-title: Mater. Today Commun. – year: 2020 ident: bib23 article-title: Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys publication-title: Mater. Des. – year: 2024 ident: bib25 article-title: Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: a multilayered approach using transfer learning and connection weights analysis publication-title: Comput. Biol. Med. – year: 2022 ident: bib3 article-title: Composition design and preparation process of refractory high-entropy alloys: a review publication-title: Int. J. Refract. Met. Hard Mater. – year: 2023 ident: bib18 article-title: Overview: recent studies of machine learning in phase prediction of high entropy alloys publication-title: Tungsten – year: 2022 ident: bib31 article-title: Design of oxygen doped TiZrHfNbTa refractory high entropy alloys with enhanced strength and ductility publication-title: Mater. Des. – year: 2024 ident: bib21 article-title: Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties publication-title: Comput. Mater. Sci. – year: 2024 ident: bib38 article-title: Microstructural and mechanical behavior of second-phase hardened porous refractory Ti-Nb-Zr-Ta alloys publication-title: J. Alloy. Compd. – year: 2025 ident: bib17 article-title: Cryo-rolling and annealing-mediated phase transformation in Al5 Ti2.5 Fe25 Cr25 Ni42.5 high-entropy alloy: experimental, phase-field and CALPHAD investigation publication-title: J. Mater. Sci. Technol. – year: 2011 ident: bib4 article-title: Phase stability in high entropy alloys: formation of solid-solution phase or amorphous phase publication-title: Progress Nat. Sci. Mater. Int. – year: 2024 ident: bib29 article-title: Development of single-phase BCC refractory high entropy alloys using machine learning techniques publication-title: Comput. Mater. Sci. – year: 2021 ident: bib19 article-title: Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data publication-title: Appl. Phys. Lett. – year: 2010 ident: bib24 article-title: Mixing enthalpy of liquid phase calculated by miedema’s scheme and approximated with sub regular solution model for assessing forming ability of amorphous and glassy alloys publication-title: Intermetallics – year: 2018 ident: bib13 article-title: Effect of atomic size difference on the microstructure and mechanical properties of high-entropy alloys publication-title: Entropy – year: 2018 ident: bib30 article-title: Phase stability of a ductile single-phase BCC Hf0.5Nb0.5Ta0.5Ti1.5Zr refractory high-entropy alloy publication-title: Intermetallics – year: 2021 ident: 10.1016/j.mtcomm.2025.113102_bib6 article-title: Predictive descriptors in machine learning and data-enabled explorations of high-entropy alloys publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2021.110381 – year: 2022 ident: 10.1016/j.mtcomm.2025.113102_bib3 article-title: Composition design and preparation process of refractory high-entropy alloys: a review publication-title: Int. J. Refract. Met. Hard Mater. – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib10 article-title: Multi-objective optimization for high-performance Fe-based metallic glasses via machine learning approach publication-title: J. Alloy. Compd. – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib35 article-title: Phase stability and mechanical properties of Ta enriched TiTaNbZrMo refractory high entropy alloys publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2024.174408 – year: 2021 ident: 10.1016/j.mtcomm.2025.113102_bib34 article-title: On the origins of ultra-high hardness and strain gradient plasticity in multi-phase nanocrystalline MoNbTaTiW based refractory high-entropy alloy publication-title: Materials Science Engineering A doi: 10.1016/j.msea.2021.141098 – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib38 article-title: Microstructural and mechanical behavior of second-phase hardened porous refractory Ti-Nb-Zr-Ta alloys publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2024.173605 – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib25 article-title: Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: a multilayered approach using transfer learning and connection weights analysis publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2024.108809 – year: 2018 ident: 10.1016/j.mtcomm.2025.113102_bib30 article-title: Phase stability of a ductile single-phase BCC Hf0.5Nb0.5Ta0.5Ti1.5Zr refractory high-entropy alloy publication-title: Intermetallics doi: 10.1016/j.intermet.2018.04.023 – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib27 article-title: Hydrogen diffusion in nickel superalloys: electrochemical permeation study and computational AI predictive modeling publication-title: Materials doi: 10.3390/ma16206622 – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib12 article-title: Machine learning-enabled prediction and optimization of hardness for Nb-Ti-V-Zr refractory high entropy alloy publication-title: Mater. Today Commun. doi: 10.1016/j.mtcomm.2024.109607 – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib2 article-title: Construction of FeCrVTiMox high-entropy alloys with enhanced mechanical properties based on electronegativity difference regulation strategy publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2023.170431 – year: 2021 ident: 10.1016/j.mtcomm.2025.113102_bib26 article-title: Evaluation of feature selection methods based on artificial neural network weights publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.114312 – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib9 article-title: Thermodynamic properties of refractory Mo-Nb-V-Ti high entropy alloys (HEAs) publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2023.173279 – year: 2021 ident: 10.1016/j.mtcomm.2025.113102_bib28 article-title: Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2021.110723 – year: 2022 ident: 10.1016/j.mtcomm.2025.113102_bib40 article-title: Design of oxygen-doped TiZrHfNbTa refractory high entropy alloys with enhanced strength and ductility publication-title: Mater. Des. doi: 10.1016/j.matdes.2022.111239 – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib43 article-title: Promoted high-temperature strength and room-temperature plasticity synergy by tuning dendrite segregation in NbMoTaW refractory high-entropy alloy publication-title: Int. J. Refract. Met. Hard Mater. – year: 2017 ident: 10.1016/j.mtcomm.2025.113102_bib41 article-title: Mechanical properties of refractory high-entropy alloys: experiments and modeling publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2016.11.188 – year: 2011 ident: 10.1016/j.mtcomm.2025.113102_bib4 article-title: Phase stability in high entropy alloys: formation of solid-solution phase or amorphous phase publication-title: Progress Nat. Sci. Mater. Int. – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib21 article-title: Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2023.112612 – year: 2025 ident: 10.1016/j.mtcomm.2025.113102_bib17 article-title: Cryo-rolling and annealing-mediated phase transformation in Al5 Ti2.5 Fe25 Cr25 Ni42.5 high-entropy alloy: experimental, phase-field and CALPHAD investigation publication-title: J. Mater. Sci. Technol. – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib18 article-title: Overview: recent studies of machine learning in phase prediction of high entropy alloys publication-title: Tungsten – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib11 article-title: Recent applications of machine learning in alloy design: a review publication-title: Materials Science Engineering R Reports doi: 10.1016/j.mser.2023.100746 – year: 2017 ident: 10.1016/j.mtcomm.2025.113102_bib8 article-title: Thermodynamic design of high-entropy refractory alloys publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2016.09.189 – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib29 article-title: Development of single-phase BCC refractory high entropy alloys using machine learning techniques publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2024.112917 – year: 2022 ident: 10.1016/j.mtcomm.2025.113102_bib31 article-title: Design of oxygen doped TiZrHfNbTa refractory high entropy alloys with enhanced strength and ductility publication-title: Mater. Des. doi: 10.1016/j.matdes.2022.111239 – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib37 article-title: Effect of Mo on high entropy Ti-Nb-Zr-Ta alloy: phase equilibria, microstructure and mechanical properties publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2023.170758 – year: 2019 ident: 10.1016/j.mtcomm.2025.113102_bib14 article-title: Machine-learning model for predicting phase formations of high-entropy alloys publication-title: Phys. Rev. Mater. doi: 10.1103/PhysRevMaterials.3.095005 – year: 2025 ident: 10.1016/j.mtcomm.2025.113102_bib36 article-title: Developing a single-phase and nanograined refractory high-entropy alloy ZrHfNbTaW with ultrahigh hardness by phase transformation via high-pressure torsion publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2024.178274 – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib16 article-title: Hardening-softening of Al0.3CoCrFeNi high-entropy alloy under nanoindentation publication-title: Mater. Des. – year: 2010 ident: 10.1016/j.mtcomm.2025.113102_bib24 article-title: Mixing enthalpy of liquid phase calculated by miedema’s scheme and approximated with sub regular solution model for assessing forming ability of amorphous and glassy alloys publication-title: Intermetallics doi: 10.1016/j.intermet.2010.06.003 – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib15 article-title: Review on applications of artificial neural networks to develop high entropy alloys: a state-of-the-art technique publication-title: Mater. Today Commun. – year: 2019 ident: 10.1016/j.mtcomm.2025.113102_bib20 article-title: Prediction of the composition and hardness of high-entropy alloys by machine learning publication-title: JOM doi: 10.1007/s11837-019-03704-4 – year: 2016 ident: 10.1016/j.mtcomm.2025.113102_bib39 article-title: Senary refractory high-entropy alloy HfNbTaTiVZr publication-title: Metall. Mater. Trans. A doi: 10.1007/s11661-015-3105-z – year: 2025 ident: 10.1016/j.mtcomm.2025.113102_bib42 article-title: Nanoindentation and orientation imaging analysis of high entropy Ti–Nb–Zr–Ta–Mo alloys exhibiting dendrite microstructure publication-title: J. Mater. Res. Technol. doi: 10.1016/j.jmrt.2025.01.150 – year: 2018 ident: 10.1016/j.mtcomm.2025.113102_bib13 article-title: Effect of atomic size difference on the microstructure and mechanical properties of high-entropy alloys publication-title: Entropy doi: 10.3390/e20120967 – year: 2019 ident: 10.1016/j.mtcomm.2025.113102_bib22 article-title: ICME approach to explore equiatomic and non equiatomic single phase BCC refractory high entropy alloys publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2019.06.387 – year: 2020 ident: 10.1016/j.mtcomm.2025.113102_bib23 article-title: Machine learning reveals the importance of the formation enthalpy and atom-size difference in forming phases of high entropy alloys publication-title: Mater. Des. – year: 2021 ident: 10.1016/j.mtcomm.2025.113102_bib19 article-title: Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data publication-title: Appl. Phys. Lett. doi: 10.1063/5.0065303 – year: 2016 ident: 10.1016/j.mtcomm.2025.113102_bib1 article-title: High-entropy alloy: challenges and prospects publication-title: Mater. Today doi: 10.1016/j.mattod.2015.11.026 – year: 2017 ident: 10.1016/j.mtcomm.2025.113102_bib33 article-title: Mechanical properties of refractory high-entropy alloys: experiments and modelling publication-title: J. Alloy. Compd. doi: 10.1016/j.jallcom.2016.11.188 – year: 2023 ident: 10.1016/j.mtcomm.2025.113102_bib5 article-title: Refractory high-entropy alloys: a focused review of preparation methods and properties publication-title: J. Mater. Sci. Technol. doi: 10.1016/j.jmst.2022.08.046 – year: 2015 ident: 10.1016/j.mtcomm.2025.113102_bib7 article-title: Design of refractory high-entropy alloys publication-title: JOM – year: 2024 ident: 10.1016/j.mtcomm.2025.113102_bib44 article-title: Influence of micro-segregation on the microstructure, and microhardness of MoNbTaxTi(1-x)W refractory high entropy alloys: experimental and DFT approach publication-title: Intermetallics doi: 10.1016/j.intermet.2023.108080 – year: 2019 ident: 10.1016/j.mtcomm.2025.113102_bib32 article-title: Phase transformations of HfNbTaTiZr high-entropy alloy at intermediate temperatures publication-title: Scr. Mater. |
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