Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach
A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learnin...
- Autores:
-
Tzu-Chia, Chen
Rajiman, Rajiman
Elveny, Marischa
Grimaldo Guerrero, John William
Lawal, Adedoyin Isola
Acwin Dwijendra, Ngakan Ketut
aravindhan, surendar
Danshina, Svetlana
ZHU, Yu
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8642
- Acceso en línea:
- https://hdl.handle.net/11323/8642
https://doi.org/10.1007/s13369-021-05966-0
https://repositorio.cuc.edu.co/
- Palabra clave:
- Bulk metallic glass
Glass-forming ability
Machine learning
Materials design
- Rights
- embargoedAccess
- License
- CC0 1.0 Universal
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Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| title |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| spellingShingle |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach Bulk metallic glass Glass-forming ability Machine learning Materials design |
| title_short |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| title_full |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| title_fullStr |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| title_full_unstemmed |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| title_sort |
Engineering of novel fe-based bulk metallic glasses using a machine learning-based approach |
| dc.creator.fl_str_mv |
Tzu-Chia, Chen Rajiman, Rajiman Elveny, Marischa Grimaldo Guerrero, John William Lawal, Adedoyin Isola Acwin Dwijendra, Ngakan Ketut aravindhan, surendar Danshina, Svetlana ZHU, Yu |
| dc.contributor.author.spa.fl_str_mv |
Tzu-Chia, Chen Rajiman, Rajiman Elveny, Marischa Grimaldo Guerrero, John William Lawal, Adedoyin Isola Acwin Dwijendra, Ngakan Ketut aravindhan, surendar Danshina, Svetlana ZHU, Yu |
| dc.subject.spa.fl_str_mv |
Bulk metallic glass Glass-forming ability Machine learning Materials design |
| topic |
Bulk metallic glass Glass-forming ability Machine learning Materials design |
| description |
A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accurately predicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe–B–Co system was selected and effects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minor addition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe–B–Co system; however, a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositions also confirmed the capability of our ML model for designing novel Fe-based BMGs. © 2021, King Fahd University of Petroleum & Minerals. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-06T21:20:14Z |
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2021-09-06T21:20:14Z |
| dc.date.issued.none.fl_str_mv |
2021-07-07 |
| dc.date.embargoEnd.none.fl_str_mv |
2022-07-07 |
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Artículo de revista |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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2193567X |
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https://hdl.handle.net/11323/8642 |
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https://doi.org/10.1007/s13369-021-05966-0 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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2193567X Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/8642 https://doi.org/10.1007/s13369-021-05966-0 https://repositorio.cuc.edu.co/ |
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Hu, F.; Yuan, C.; Luo, Q.; Yang, W.; Shen, B.: Effects of heavy rare-earth addition on glass-forming ability, thermal, magnetic, and mechanical properties of Fe-RE-B-Nb (RE = Dy, Ho, Er or Tm) bulk metallic glass. J. Non-cryst. Solids 525, 119681 (2019). https://doi.org/10.1016/j.jnoncrysol.2019.119681 Zheng, H.; Zhu, L.; Jiang, S.S.; Wang, Y.G.; Liu, S.N.; Lan, S.; Chen, F.G.: Role of Ni and Co in tailoring magnetic and mechanical properties of Fe84Si2B13P1 metallic glass. J. Alloys Compd. 816, 152549 (2020). https://doi.org/10.1016/j.jallcom.2019.152549 Chen, S.-Q.; Li, M.; Ma, X.-Y.; Zhou, M.-J.; Wang, D.; Yan, M.-Y.; Li, Z.; Yao, K.-F.: Influence of inorganic ions on degradation capability of Fe-based metallic glass towards dyeing wastewater remediation. Chemosphere 264, 128392 (2021). https://doi.org/10.1016/j.chemosphere.2020.128392 Chen, S.-Q.; Hui, K.-Z.; Dong, L.-Z.; Li, Z.; Zhang, Q.; Gu, L.; Zhao, W.; Lan, S.; Ke, Y.; Shao, Y.; Hahn, H.; Yao, K.-F.: Excellent long-term reactivity of inhomogeneous nanoscale Fe-based metallic glass in wastewater purification. Sci. China Mater. 63, 453–466 (2020). https://doi.org/10.1007/s40843-019-1205-5 Chen, H.; Dong, B.; Zhou, S.; Li, X.; Qin, J.: Structural, magnetic, and electronic properties of Fe82Si4B10P4 metallic glass. Sci. Rep. 8, 5680 (2018). https://doi.org/10.1038/s41598-018-23952-9 Li, H.X.; Lu, Z.C.; Wang, S.L.; Wu, Y.; Lu, Z.P.: Fe-based bulk metallic glasses: glass formation, fabrication, properties and applications. Prog. Mater. Sci. 103, 235–318 (2019). https://doi.org/10.1016/j.pmatsci.2019.01.003 Zhang, P.C.; Chang, J.; Wang, H.P.: Transition from crystal to metallic glass and micromechanical property change of Fe-B-Si alloy during rapid solidification. Metall. Mater. Trans. B 51, 327–337 (2020). https://doi.org/10.1007/s11663-019-01748-0 Li, H.X.; Li, C.Q.; Cao, D.; Yang, W.M.; Li, Q.; Lu, Z.P.: Influences of oxygen on plastic deformation of a Fe-based bulk metallic glass. Scr. Mater. 135, 24–28 (2017). https://doi.org/10.1016/j.scriptamat.2017.03.018 Lesz, S.: Effect of cooling rates on the structure, density and micro-indentation behavior of the Fe, Co-based bulk metallic glass. Mater. Charact. 124, 97–106 (2017). https://doi.org/10.1016/j.matchar.2016.12.016 Liang, D.; Wei, X.; Chang, C.; Li, J.; Wang, X.; Shen, J.: Effect of W addition on the glass forming ability and mechanical properties of Fe-based metallic glass. J. Alloys Compd. 731, 1146–1150 (2018). https://doi.org/10.1016/j.jallcom.2017.10.104 Ouyang, D.; Xing, W.; Li, N.; Li, Y.; Liu, L.: Structural evolutions in 3D-printed Fe-based metallic glass fabricated by selective laser melting. Addit. Manuf. 23, 246–252 (2018). https://doi.org/10.1016/j.addma.2018.08.020 Liu, X.; Li, X.; He, Q.; Liang, D.; Zhou, Z.; Ma, J.; Yang, Y.; Shen, J.: Machine learning-based glass formation prediction in multicomponent alloys. Acta Mater. 201, 182–190 (2020). https://doi.org/10.1016/j.actamat.2020.09.081 Xiong, J.; Zhang, T.-Y.; Shi, S.-Q.: Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses. MRS Commun. 9, 576–585 (2019). https://doi.org/10.1557/mrc.2019.44 Lu, X.; Deng, L.; Du, J.; Vienna, J.D.: Predicting boron coordination in multicomponent borate and borosilicate glasses using analytical models and machine learning. J. Non-cryst. Solids 553, 120490 (2021). https://doi.org/10.1016/j.jnoncrysol.2020.120490 Cai, A.; Xiong, X.; Liu, Y.; An, W.; Tan, J.; Luo, Y.: Artificial neural network modeling for undercooled liquid region of glass forming alloys. Comput. Mater. Sci. 48, 109–114 (2010). https://doi.org/10.1016/j.commatsci.2009.12.012 Tripathi, M.K.; Chattopadhyay, P.P.; Ganguly, S.: Multivariate analysis and classification of bulk metallic glasses using principal component analysis. Comput. Mater. Sci. 107, 79–87 (2015). https://doi.org/10.1016/j.commatsci.2015.05.010 Tripathi, M.K.; Chattopadhyay, P.P.; Ganguly, S.: A predictable glass forming ability expression by statistical learning and evolutionary intelligence. Intermetallics 90, 9–15 (2017). https://doi.org/10.1016/j.intermet.2017.06.008 Alcobaça, E.; Mastelini, S.M.; Botari, T.; Pimentel, B.A.; Cassar, D.R.; de Carvalho, A.C.P.L.F.; Zanotto, E.D.: Explainable machine learning algorithms for predicting glass transition temperatures. Acta Mater. 188, 92–100 (2020). https://doi.org/10.1016/j.actamat.2020.01.047 Ward, L.; O’Keeffe, S.C.; Stevick, J.; Jelbert, G.R.; Aykol, M.; Wolverton, C.: A machine learning approach for engineering bulk metallic glass alloys. Acta Mater. 159, 102–111 (2018). https://doi.org/10.1016/j.actamat.2018.08.002 Samavatian, M.; Gholamipour, R.; Samavatian, V.: Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach. Comput. Mater. Sci. 186, 110025 (2021). https://doi.org/10.1016/j.commatsci.2020.110025 Ren, F.; Ward, L.; Williams, T.; Laws, K.J.; Wolverton, C.; Hattrick-Simpers, J.; Mehta, A.: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, eaaq1566 (2018). https://doi.org/10.1126/sciadv.aaq1566 Dasgupta, A.; Broderick, S.R.; Mack, C.; Kota, B.U.; Subramanian, R.; Setlur, S.; Govindaraju, V.; Rajan, K.: Probabilistic assessment of glass forming ability rules for metallic glasses aided by automated analysis of phase diagrams. Sci. Rep. 9, 357 (2019). https://doi.org/10.1038/s41598-018-36224-3 Kawazoe, Y.; Yu, J.-Z.; Tsai, A.-P.; Masumoto, T.: Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys, 1st edn. Springer-Verlag, Berlin, Heidelberg (1997) Kawazoe, Y.; Yu, J.-Z.; Tsai, A.-P.; Masumoto, T.: Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys, 1st edn. Springer-Verlag, Berlin, Heidelberg (1997) Lu, Z.P.; Tan, H.; Li, Y.; Ng, S.C.: Correlation between reduced glass transition temperature and glass forming ability of bulk metallic glasses (2000) Li, Y.: A relationship between glass-forming ability and reduced glass transition temperature near eutectic composition. Mater. Trans. 42, 556–561 (2001) Xiong, J.; Shi, S.-Q.; Zhang, T.-Y.: A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mater. Des. 187, 108378 (2020). https://doi.org/10.1016/j.matdes.2019.108378 Samavatian, V.; Fotuhi-Firuzabad, M.; Samavatian, M.; Dehghanian, P.; Blaabjerg, F.: Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics. Sci. Rep. 10, 14821 (2020). https://doi.org/10.1038/s41598-020-71926-7 Joress, H.; DeCost, B.L.; Sarker, S.; Braun, T.M.; Jilani, S.; Smith, R.; Ward, L.; Laws, K.J.; Mehta, A.; Hattrick-Simpers, J.R.: A high-throughput structural and electrochemical study of metallic glass formation in Ni–Ti–Al. ACS Comb. Sci. 22, 330–338 (2020). https://doi.org/10.1021/acscombsci.9b00215 Śniadecki, Z.: Glass-forming ability of Fe-Ni alloys substituted by group V and VI transition metals (V, Nb, Cr, Mo) studied by thermodynamic modeling. Metall. Mater. Trans. A 51, 4777–4785 (2020). https://doi.org/10.1007/s11661-020-05897-9 Jiang, Q.; Chi, B.Q.; Li, J.C.: A valence electron concentration criterion for glass-formation ability of metallic liquids. Appl. Phys. Lett. 82, 2984–2986 (2003). https://doi.org/10.1063/1.1571984 Zhou, C.; Guo, C.; Li, C.; Du, Z.: Thermodynamic assessment of the phase equilibria and prediction of glass-forming ability of the Al–Cu–Zr system. J. Non-cryst. Solids 461, 47–60 (2017). https://doi.org/10.1016/j.jnoncrysol.2016.09.031 Ganorkar, S.; Lee, Y.-H.; Lee, S.; Cho, Y.C.; Ishikawa, T.; Lee, G.W.: Unequal effect of thermodynamics and kinetics on glass forming ability of Cu–Zr alloys. AIP Adv. 10, 45114 (2020). https://doi.org/10.1063/5.0002784 Hu, Y.-C.; Tanaka, H.: Physical origin of glass formation from multicomponent systems. Sci. Adv. 6, eabd2928 (2020). https://doi.org/10.1126/sciadv.abd2928 Ojovan, M.I.: Glass formation. Encycl. Glas. Sci. Technol. Hist. Cult. (2021). https://doi.org/10.1002/9781118801017.ch3.1 Mukherjee, S.; Schroers, J.; Zhou, Z.; Johnson, W.L.; Rhim, W.-K.: Viscosity and specific volume of bulk metallic glass-forming alloys and their correlation with glass forming ability. Acta Mater. 52, 3689–3695 (2004). https://doi.org/10.1016/j.actamat.2004.04.023 Louzguine-Luzgin, D.V.; Inoue, A.: Bulk metallic glasses. Encycl. Glas. Sci. Technol. Hist. Cult. (2021). https://doi.org/10.1002/9781118801017.ch7.10 Fujita, T.; Konno, K.; Zhang, W.; Kumar, V.; Matsuura, M.; Inoue, A.; Sakurai, T.; Chen, M.W.: Atomic-scale heterogeneity of a multicomponent bulk metallic glass with excellent glass forming ability. Phys. Rev. Lett. 103, 75502 (2009). https://doi.org/10.1103/PhysRevLett.103.075502 Li, M.; Guan, H.; Yang, S.; Ma, X.; Li, Q.: Minor Cr alloyed Fe–Co–Ni–P–B high entropy bulk metallic glass with excellent mechanical properties. Mater. Sci. Eng. A (2020). https://doi.org/10.1016/j.msea.2020.140542 |
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Tzu-Chia, ChenRajiman, RajimanElveny, MarischaGrimaldo Guerrero, John WilliamLawal, Adedoyin IsolaAcwin Dwijendra, Ngakan Ketutaravindhan, surendarDanshina, SvetlanaZHU, Yu2021-09-06T21:20:14Z2021-09-06T21:20:14Z2021-07-072022-07-072193567Xhttps://hdl.handle.net/11323/8642https://doi.org/10.1007/s13369-021-05966-0Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accurately predicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe–B–Co system was selected and effects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minor addition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe–B–Co system; however, a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositions also confirmed the capability of our ML model for designing novel Fe-based BMGs. © 2021, King Fahd University of Petroleum & Minerals.Tzu-Chia, ChenRajiman, RajimanElveny, Marischa-will be generated-orcid-0000-0003-2014-6173-600Grimaldo Guerrero, John William-will be generated-orcid-0000-0002-1632-5374-600Lawal, Adedoyin Isola-will be generated-orcid-0000-0001-8295-1560-600Acwin Dwijendra, Ngakan Ketutaravindhan, surendar-will be generated-orcid-0000-0002-2421-9192-600Danshina, Svetlana-will be generated-orcid-0000-0002-9467-078X-600ZHU, Yu-will be generated-orcid-0000-0003-2204-0946-600application/pdfspaArabian Journal for Science and EngineeringCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfArabian Journal for Science and Engineeringhttps://link.springer.com/article/10.1007%2Fs13369-021-05966-0Bulk metallic glassGlass-forming abilityMachine learningMaterials designEngineering of novel fe-based bulk metallic glasses using a machine learning-based approachArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionHu, F.; Yuan, C.; Luo, Q.; Yang, W.; Shen, B.: Effects of heavy rare-earth addition on glass-forming ability, thermal, magnetic, and mechanical properties of Fe-RE-B-Nb (RE = Dy, Ho, Er or Tm) bulk metallic glass. J. Non-cryst. Solids 525, 119681 (2019). https://doi.org/10.1016/j.jnoncrysol.2019.119681Zheng, H.; Zhu, L.; Jiang, S.S.; Wang, Y.G.; Liu, S.N.; Lan, S.; Chen, F.G.: Role of Ni and Co in tailoring magnetic and mechanical properties of Fe84Si2B13P1 metallic glass. J. Alloys Compd. 816, 152549 (2020). https://doi.org/10.1016/j.jallcom.2019.152549Chen, S.-Q.; Li, M.; Ma, X.-Y.; Zhou, M.-J.; Wang, D.; Yan, M.-Y.; Li, Z.; Yao, K.-F.: Influence of inorganic ions on degradation capability of Fe-based metallic glass towards dyeing wastewater remediation. Chemosphere 264, 128392 (2021). https://doi.org/10.1016/j.chemosphere.2020.128392Chen, S.-Q.; Hui, K.-Z.; Dong, L.-Z.; Li, Z.; Zhang, Q.; Gu, L.; Zhao, W.; Lan, S.; Ke, Y.; Shao, Y.; Hahn, H.; Yao, K.-F.: Excellent long-term reactivity of inhomogeneous nanoscale Fe-based metallic glass in wastewater purification. Sci. China Mater. 63, 453–466 (2020). https://doi.org/10.1007/s40843-019-1205-5Chen, H.; Dong, B.; Zhou, S.; Li, X.; Qin, J.: Structural, magnetic, and electronic properties of Fe82Si4B10P4 metallic glass. Sci. Rep. 8, 5680 (2018). https://doi.org/10.1038/s41598-018-23952-9Li, H.X.; Lu, Z.C.; Wang, S.L.; Wu, Y.; Lu, Z.P.: Fe-based bulk metallic glasses: glass formation, fabrication, properties and applications. Prog. Mater. Sci. 103, 235–318 (2019). https://doi.org/10.1016/j.pmatsci.2019.01.003Zhang, P.C.; Chang, J.; Wang, H.P.: Transition from crystal to metallic glass and micromechanical property change of Fe-B-Si alloy during rapid solidification. Metall. Mater. Trans. B 51, 327–337 (2020). https://doi.org/10.1007/s11663-019-01748-0Li, H.X.; Li, C.Q.; Cao, D.; Yang, W.M.; Li, Q.; Lu, Z.P.: Influences of oxygen on plastic deformation of a Fe-based bulk metallic glass. Scr. Mater. 135, 24–28 (2017). https://doi.org/10.1016/j.scriptamat.2017.03.018Lesz, S.: Effect of cooling rates on the structure, density and micro-indentation behavior of the Fe, Co-based bulk metallic glass. Mater. Charact. 124, 97–106 (2017). https://doi.org/10.1016/j.matchar.2016.12.016Liang, D.; Wei, X.; Chang, C.; Li, J.; Wang, X.; Shen, J.: Effect of W addition on the glass forming ability and mechanical properties of Fe-based metallic glass. J. Alloys Compd. 731, 1146–1150 (2018). https://doi.org/10.1016/j.jallcom.2017.10.104Ouyang, D.; Xing, W.; Li, N.; Li, Y.; Liu, L.: Structural evolutions in 3D-printed Fe-based metallic glass fabricated by selective laser melting. Addit. Manuf. 23, 246–252 (2018). https://doi.org/10.1016/j.addma.2018.08.020Liu, X.; Li, X.; He, Q.; Liang, D.; Zhou, Z.; Ma, J.; Yang, Y.; Shen, J.: Machine learning-based glass formation prediction in multicomponent alloys. Acta Mater. 201, 182–190 (2020). https://doi.org/10.1016/j.actamat.2020.09.081Xiong, J.; Zhang, T.-Y.; Shi, S.-Q.: Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses. MRS Commun. 9, 576–585 (2019). https://doi.org/10.1557/mrc.2019.44Lu, X.; Deng, L.; Du, J.; Vienna, J.D.: Predicting boron coordination in multicomponent borate and borosilicate glasses using analytical models and machine learning. J. Non-cryst. Solids 553, 120490 (2021). https://doi.org/10.1016/j.jnoncrysol.2020.120490Cai, A.; Xiong, X.; Liu, Y.; An, W.; Tan, J.; Luo, Y.: Artificial neural network modeling for undercooled liquid region of glass forming alloys. Comput. Mater. Sci. 48, 109–114 (2010). https://doi.org/10.1016/j.commatsci.2009.12.012Tripathi, M.K.; Chattopadhyay, P.P.; Ganguly, S.: Multivariate analysis and classification of bulk metallic glasses using principal component analysis. Comput. Mater. Sci. 107, 79–87 (2015). https://doi.org/10.1016/j.commatsci.2015.05.010Tripathi, M.K.; Chattopadhyay, P.P.; Ganguly, S.: A predictable glass forming ability expression by statistical learning and evolutionary intelligence. Intermetallics 90, 9–15 (2017). https://doi.org/10.1016/j.intermet.2017.06.008Alcobaça, E.; Mastelini, S.M.; Botari, T.; Pimentel, B.A.; Cassar, D.R.; de Carvalho, A.C.P.L.F.; Zanotto, E.D.: Explainable machine learning algorithms for predicting glass transition temperatures. Acta Mater. 188, 92–100 (2020). https://doi.org/10.1016/j.actamat.2020.01.047Ward, L.; O’Keeffe, S.C.; Stevick, J.; Jelbert, G.R.; Aykol, M.; Wolverton, C.: A machine learning approach for engineering bulk metallic glass alloys. Acta Mater. 159, 102–111 (2018). https://doi.org/10.1016/j.actamat.2018.08.002Samavatian, M.; Gholamipour, R.; Samavatian, V.: Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach. Comput. Mater. Sci. 186, 110025 (2021). https://doi.org/10.1016/j.commatsci.2020.110025Ren, F.; Ward, L.; Williams, T.; Laws, K.J.; Wolverton, C.; Hattrick-Simpers, J.; Mehta, A.: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, eaaq1566 (2018). https://doi.org/10.1126/sciadv.aaq1566Dasgupta, A.; Broderick, S.R.; Mack, C.; Kota, B.U.; Subramanian, R.; Setlur, S.; Govindaraju, V.; Rajan, K.: Probabilistic assessment of glass forming ability rules for metallic glasses aided by automated analysis of phase diagrams. Sci. Rep. 9, 357 (2019). https://doi.org/10.1038/s41598-018-36224-3Kawazoe, Y.; Yu, J.-Z.; Tsai, A.-P.; Masumoto, T.: Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys, 1st edn. Springer-Verlag, Berlin, Heidelberg (1997)Kawazoe, Y.; Yu, J.-Z.; Tsai, A.-P.; Masumoto, T.: Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys, 1st edn. Springer-Verlag, Berlin, Heidelberg (1997)Lu, Z.P.; Tan, H.; Li, Y.; Ng, S.C.: Correlation between reduced glass transition temperature and glass forming ability of bulk metallic glasses (2000)Li, Y.: A relationship between glass-forming ability and reduced glass transition temperature near eutectic composition. Mater. Trans. 42, 556–561 (2001)Xiong, J.; Shi, S.-Q.; Zhang, T.-Y.: A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mater. Des. 187, 108378 (2020). https://doi.org/10.1016/j.matdes.2019.108378Samavatian, V.; Fotuhi-Firuzabad, M.; Samavatian, M.; Dehghanian, P.; Blaabjerg, F.: Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics. Sci. Rep. 10, 14821 (2020). https://doi.org/10.1038/s41598-020-71926-7Joress, H.; DeCost, B.L.; Sarker, S.; Braun, T.M.; Jilani, S.; Smith, R.; Ward, L.; Laws, K.J.; Mehta, A.; Hattrick-Simpers, J.R.: A high-throughput structural and electrochemical study of metallic glass formation in Ni–Ti–Al. ACS Comb. Sci. 22, 330–338 (2020). https://doi.org/10.1021/acscombsci.9b00215Śniadecki, Z.: Glass-forming ability of Fe-Ni alloys substituted by group V and VI transition metals (V, Nb, Cr, Mo) studied by thermodynamic modeling. Metall. Mater. Trans. A 51, 4777–4785 (2020). https://doi.org/10.1007/s11661-020-05897-9Jiang, Q.; Chi, B.Q.; Li, J.C.: A valence electron concentration criterion for glass-formation ability of metallic liquids. Appl. Phys. Lett. 82, 2984–2986 (2003). https://doi.org/10.1063/1.1571984Zhou, C.; Guo, C.; Li, C.; Du, Z.: Thermodynamic assessment of the phase equilibria and prediction of glass-forming ability of the Al–Cu–Zr system. J. Non-cryst. Solids 461, 47–60 (2017). https://doi.org/10.1016/j.jnoncrysol.2016.09.031Ganorkar, S.; Lee, Y.-H.; Lee, S.; Cho, Y.C.; Ishikawa, T.; Lee, G.W.: Unequal effect of thermodynamics and kinetics on glass forming ability of Cu–Zr alloys. AIP Adv. 10, 45114 (2020). https://doi.org/10.1063/5.0002784Hu, Y.-C.; Tanaka, H.: Physical origin of glass formation from multicomponent systems. Sci. Adv. 6, eabd2928 (2020). https://doi.org/10.1126/sciadv.abd2928Ojovan, M.I.: Glass formation. Encycl. Glas. Sci. Technol. Hist. Cult. (2021). https://doi.org/10.1002/9781118801017.ch3.1Mukherjee, S.; Schroers, J.; Zhou, Z.; Johnson, W.L.; Rhim, W.-K.: Viscosity and specific volume of bulk metallic glass-forming alloys and their correlation with glass forming ability. Acta Mater. 52, 3689–3695 (2004). https://doi.org/10.1016/j.actamat.2004.04.023Louzguine-Luzgin, D.V.; Inoue, A.: Bulk metallic glasses. Encycl. Glas. Sci. Technol. Hist. Cult. (2021). https://doi.org/10.1002/9781118801017.ch7.10Fujita, T.; Konno, K.; Zhang, W.; Kumar, V.; Matsuura, M.; Inoue, A.; Sakurai, T.; Chen, M.W.: Atomic-scale heterogeneity of a multicomponent bulk metallic glass with excellent glass forming ability. Phys. Rev. Lett. 103, 75502 (2009). https://doi.org/10.1103/PhysRevLett.103.075502Li, M.; Guan, H.; Yang, S.; Ma, X.; Li, Q.: Minor Cr alloyed Fe–Co–Ni–P–B high entropy bulk metallic glass with excellent mechanical properties. Mater. Sci. Eng. 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