iamrp.net-atG2-1791491020-l505529596 1791491020 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2024 3 2 Emily Johnson Robert Smith Laura Brown Kevin Miller 2024 10.68159/l505529596 https://iamrp.net/pub/journal/2/article/l505529596 Gorsse S, Tancret F. Current and emerging practices of CALPHAD toward the development of high entropy alloys and complex concentrated alloys. J Mater Res. 2018;33(19):2899-923. 10.1557/jmr.2018.152 Ryzhov VN, Tareyeva EE, Fomin YD, Tsiok EN. Complex phase diagrams of systems with isotropic potentials: Results of computer simulations. Phys Usp. 2020;63(5):417-39. 10.3367/UFNe.2018.04.038417 Li T, Wang S, Fan W, Lu Y, Wang T, Li T, et al. CALPHAD-aided design for superior thermal stability and mechanical behavior in a TiZrHfNb refractory high-entropy alloy. Acta Mater. 2023;246:118728. 10.1016/j.actamat.2023.118728 Ghassemali E, Conway PLJ. High-throughput CALPHAD: A powerful tool towards accelerated metallurgy. Front Mater. 2022;9:889771. 10.3389/fmats.2022.889771 Liu X, Zhang J, Pei Z. Machine learning for high-entropy alloys: Progress, challenges and opportunities. Prog Mater Sci. 2023;131:101018. 10.1016/j.pmatsci.2022.101018 Zhou ZH. Machine learning. Singapore: Springer; 2021. 10.1007/978-981-15-1967-3 Zhang C, Yang Y. The CALPHAD approach for HEAs: Challenges and opportunities. MRS Bull. 2022;47(2):158-67. 10.1557/s43577-022-00284-8 Taheri-Mousavi SM, Xu M, Hengsbach F, Houser C, Ge Z, Glaser B, et al. Additively manufacturable high-strength aluminum alloys with thermally stable microstructures enabled by hybrid machine learning-based design. arXiv [Preprint]. 2024. 10.48550/arXiv.2406.17457 van de Walle A, Sun R, Hong QJ, Kadkhodaei S. Software tools for high-throughput CALPHAD from first-principles data. Calphad. 2017;58:70-81. 10.1016/j.calphad.2017.05.005 Hsu HHH, Shen Y, Tomani C, Cremers D. What makes graph neural networks miscalibrated? Adv Neural Inf Process Syst. 2022;35:13775-86. Zeng Y, Man M, Bai K, Zhang YW. Revealing high-fidelity phase selection rules for high entropy alloys: A combined CALPHAD and machine learning study. Mater Des. 2021;202:109532. 10.1016/j.matdes.2021.109532 Zou H, Tian YY, Zhang LG, Xue RH, Deng ZX, Lu MM, et al. Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys. Rare Met. 2024;43(1):309-23. 10.1007/s12598-023-02333-w Deffrennes G, Terayama K, Abe T, Tamura R. A machine learning-based classification approach for phase diagram prediction. Mater Des. 2022;215:110497. 10.1016/j.matdes.2022.110497 Li W, Raman L, Debnath A, Ahn M, Lin S, Krajewski AM, et al. Design and validation of refractory alloys using machine learning, CALPHAD, and experiments. Int J Refract Met Hard Mater. 2024;121:106673. 10.1016/j.ijrmhm.2024.106673 Jha R, Chakraborti N, Diercks DR, Stebner AP, Ciobanu CV. Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys. Comput Mater Sci. 2018;150:202-11. 10.1016/j.commatsci.2018.04.008 Bansal A, Kumar P, Yadav S, Hariharan VS, Rahul MR, Phanikumar G. Accelerated design of high entropy alloys by integrating high throughput calculation and machine learning. J Alloys Compd. 2023;960:170543. 10.1016/j.jallcom.2023.170543 Zhang W, Tang Y, Gao J, Zhang L, Ding J, Xia X. Determination of hardness and Young's modulus in fcc Cu-Ni-Sn-Al alloys via high-throughput experiments, CALPHAD approach and machine learning. J Mater Res Technol. 2024;30:5381-93. 10.1016/j.jmrt.2024.04.221 Liu F, Xiao X, Huang L, Tan L, Liu Y. Design of NiCoCrAl eutectic high entropy alloys by combining machine learning with CALPHAD method. Mater Today Commun. 2022;30:103172. 10.1016/j.mtcomm.2022.103172 Alpaydin E. Machine learning. Revised and updated ed. Cambridge (MA): MIT Press; 2021. 280 p. Odetola PI, Babalola BJ, Afolabi AE, Anamu US, Olorundaisi E, Umba MC, et al. Exploring high entropy alloys: A review on thermodynamic design and computational modeling strategies for advanced materials applications. Heliyon. 2024;10(22):e39660. 10.1016/j.heliyon.2024.e39660 Guo L, Gu J, Gong X, Ni S, Song M. CALPHAD-aided design of high entropy alloy to achieve high strength via precipitate strengthening. Sci China Mater. 2020;63(2):288-99. 10.1007/s40843-019-1170-7 Cao X, Luo W, Liu H. A prediction model for CO2/CO adsorption performance on binary alloys based on machine learning. RSC Adv. 2024;14(17):12235-46. 10.1039/D4RA00710G Wang Y, Li K, Soisson F, Becquart CS. Combining DFT and CALPHAD for the development of on-lattice interaction models: The case of Fe-Ni system. Phys Rev Mater. 2020;4(11):113801. 10.1103/PhysRevMaterials.4.113801 Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun. 2022;13(1):2453. 10.1038/s41467-022-29939-5 Gao J, Zhong J, Liu G, Yang S, Song B, Zhang L, et al. A machine learning accelerated distributed task management system (Malac-Distmas) and its application in high-throughput CALPHAD computations aiming at efficient alloy design. Adv Powder Mater. 2022;1(1):100005. 10.1016/j.apmate.2021.09.005 Ruan J, Xu W, Yang T, Yu J, Yang S, Luan J, et al. Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods. Acta Mater. 2020;186:425-33. 10.1016/j.actamat.2020.01.004 Korotaev P, Yanilkin A. Steels classification by machine learning and CALPHAD methods. Calphad. 2023;82:102587. 10.1016/j.calphad.2023.102587 Paulus K. Combined CALPHAD and machine learning for property modelling [master's thesis]. Stockholm: KTH Royal Institute of Technology; 2020. Available from: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278149