Accelerated discovery of high-strength aluminum alloys by machine learning

ARTICLE https://doi.org/10.1038/s43246-020-00074-2 OPEN 1234567890():,; Accelerated discovery of high-strength aluminum alloys by machine learning Jiaheng Li 1, Yingbo Zhang 1 ✉, Xinyu Cao1, Qi Zeng1, Ye Zhuang1, Xiaoying Qian1 & Hui Chen1 Aluminum alloys are attractive for a number of applications due to their high specific strength, and developing new compositions is a major goal in the structural materials community. Here, we investigate the Al-Zn-Mg-Cu alloy system (7xxx series) by machine learning-based composition and process optimization. The discovered optimized alloy is compositionally lean with a high ultimate tensile strength of 952 MPa and 6.3% elongation following a cost-effective processing route. We find that the Al8Cu4Y phase in wrought 7xxxT6 alloys exists in the form of a nanoscale network structure along sub-grain boundaries besides the common irregular-shaped particles. Our study demonstrates the feasibility of using machine learning to search for 7xxx alloys with good mechanical performance. 1 Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, 610031 Chengdu, China. ✉email: COMMUNICATIONS MATERIALS | (2020)1:73 | https://doi.org/10.1038/s43246-020-00074-2 | www.nature.com/commsmat 1 ARTICLE COMMUNICATIONS MATERIALS | https://doi.org/10.1038/s43246-020-00074-2 A l–Zn–Mg–Cu (7xxx series) alloys have been vastly used in the aerospace industry and have shown increasingly appreciable potentials in rail transportation due to their superior physiomechanical properties and manufacturability1–3. In recent decades, some emerging engineering materials (e.g., magnesium alloys, titanium alloys, and composites) have achieved rapid developments and challenged 7xxx alloys in many fields4. Therefore, further performance advancements are deeply required for 7xxx alloys to persistently remain competitive in their dominant fields and to get more opportunities in new application areas. Mechanical strength is a basic consideration for structural materials. The ultimate tensile strength (UTS) of commercial 7xxx alloys is typically below 700 MPa4. Interests in improving the UTS of 7xxx alloys have never ceased. Some sophisticated techniques [e.g., severe plastic deformation (SPD), rapid solidification and powder metallurgy (RS/PM), spray forming, and multistage heat treatments] have pushed the UTS of 7xxx alloys to extremely high levels, in excess of 750 MPa4–10. For instance, an ultrafine-grained 7475 alloy and a nano-grained 7075 alloy processed by high-pressure torsion (HPT, an SPD processing), manifested UTSs of more than 900 MPa5,11. However, these techniques are now confined by their inabilities to fabricate largesize products, complex operations, high costs, or high requirements for facilities, which limit their extensive industrial uses. To develop high-strength 7xxx alloys aimed at industrializing, optimizing the alloy composition could be a practical strategy. 7xxx alloys usually contain main alloying elements of Zn, Mg, and Cu, as well as trace elements of Cr, Mn, Zr, Ti, Sc, etc. Many efforts have been made to tailor the contents of the main alloying elements for excellent mechanical properties12–14. It now appears that high Zn and Mg contents (Zn > 8 wt.%; Mg > 2.0 wt.%) are necessary for ensuring ultra-high strength, but they synchronously increase localized corrosion susceptibility, and the hot tearing susceptibility (a catastrophic problem in industrial production) and macro-segregation during casting12,15–17. As an effective grain refiner and an anti-recrystallization agent, Zr is a nearly indispensable trace element in 7xxx alloys. High-strength 7xxx alloys containing 0.1–0.2 wt.% Zr are often present18. Moreover, combined addition of Zr and Ti has a better strengthening effect than single addition of Zr thanks to the formation of L12-Al3(Zrx,Ti1-x), which is more stable than L12Al3Zr dispersoids19. Micro-alloying of rare-earth elements (e.g., Sc, Yb, Y, Ce, and Gd) is a potent approach to modifying the microstructure and properties of 7xxx alloys given its grain refinement and recrystallization inhibition effects20–23. Among all Fig. 1 Research roadmap of this study. Machine-learning-assisted composition optimization and a range of traditional processing techniques are integrated for the rapid discovery of Al–Zn–Mg–Cu–Ti–(Y)–(Ce) alloys with desired ultimate tensile strength (UTS) and characterizations of the optimized Al–Zn–Mg–Cu–Zr–Ti–(Y)–(Ce) alloy, respectively. 2 the frequently-used rare-earth elements in 7xxx alloys, Sc element is deemed as the most effective. Nevertheless, it is imperative to research and find other rare-earth elements cheaper than Sc (e.g., Y and Ce) to replace Sc owing to the prohibitive price of Sc. In a word, combined addition of multiple alloying elements is an important development trend of 7xxx alloys. Given the wide composition range24 and composition-sensitivity of 7xxx alloys, numerous undiscovered alloys may outperform the existing counterparts (regardless of possible strengthening effects of rareearth elements). The vast unexplored composition space, thus, presents a good opportunity to develop 7xxx alloys with desired properties. However, in combination with their numerous processing steps, 7xxx alloys are extremely complex and thus hard to optimize via intuition and trial-and-error. Recently, machinelearning-assisted design of multicomponent materials has attracted special interests25–31. We believe that machine learning could play an important role in composition optimization of multicomponent 7xxx alloys, although related publications are limited32. Our objective here is to discover new 7xxx alloys with desired UTS by machine learning. Here we propose a modified, Kriging model-based33 efficient global optimization (EGO) algorithm34 and apply it to composition optimization of 7xxx alloys. A 950 MPa grade 7xxx alloy was developed with Zn content of less than 7 wt.%. Meanwhile, we found the unusual formation of an Al8Cu4Y nanoscale network structure in wrought 7xxx-T6 alloys, which may be useful for future alloy design of high-strength aluminum alloys. This study demonstrates the feasibility of using machine learning to search for 7xxx alloys with good mechanical performance. Alloys based on the optimized alloy will be candidates for the mass production of a critical part in high-speed trains. Results Summary of the research strategy. Our research roadmap is shown in Fig. 1. First, we prepare a training data set for subsequent model evaluations and constructions. It contains some selected Al–Zn–Mg–Cu–(Ti)–(Y)–(Ce) alloys with known UTSs. Then, we evaluate or validate the machine-learning model through “leave-one-out cross-validation”34—where one observation in the original data set is treated as a test point and predicted back based the remaining observations—to determine its feasibility. An iterative process (...truncated)