Aluminum-ion batteries have garnered significant interest as a potentially safer and cheaper replacement for conventional lithium-ion batteries, offering a shorter charging time and denser storage capacity. Nonetheless, the progress in this field is considerably hampered by the limited availability of suitable cathode materials that can sustain the reversible intercalation of Al 3+ /[AlCl 4 ] − ions, particularly after long cycles. Herein, we demonstrate that rechargeable Al batteries embedded with two-dimensional (2D) Nb 2 CT x MXene as a cathode material exhibit excellent capacity and exceptional long cyclic performance. We have successfully improved the initial electrochemical performance of Nb 2 CT x MXene after being properly delaminated to a single-layered microstructure and subjected to a postsynthesis calcining treatment. Compared to pristine Nb 2 CT x MXene, the Al battery embedded with the calcined Nb 2 CT x MXene cathode has, respectively, retained high capacities of 108 and 80 mAh g −1 after 500 cycles at current densities of 0.2 and 0.5 A g −1 in a wide voltage window (0.1−2.4 V). Noteworthily, the cyclic lifetime of Nb 2 CT x MXene was extended from ∼300 to >500 times after calcination. We reveal that attaining Nb 2 CT x nanosheets with a controllable d-spacing has promoted the migration of the [AlCl 4 ] − and Al 3+ ions in the MXene interlayers, leading to enhanced charge storage. Furthermore, we found out that the formation of niobium oxides and amorphous carbon after calcination probably benefits the electrochemical performance of Nb 2 CT x MXene electrode in Al batteries.

In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor.

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As promising next-generation candidates for applications in aero-engines, L12-strengthened cobalt (Co)-based superalloys have attracted extensive attention. However, the L12 strengthening phase in first-generation Co-Al-W-based superalloys is metastable and both its solvus temperature and mechanical properties still need to be improved. Therefore, it is necessary to discover new L12-strengthened Co-based superalloy systems with a stable L12 phase by exploring the effect of alloying elements on its stability. Traditional first-principles calculations are capable of providing the crystal structure and mechanical properties of the L12 phase doped by transition metals but suffer from low efficiency and relatively high computational costs. The present study combines machine learning (ML) with first-principles calculations to accelerate crystal structure and mechanical property predictions, with the latter providing both the training and validation datasets. Three ML models are established and trained for predicting the occupancy of alloying elements in the supercell and the stability and the mechanical properties of the L12 phase. The ML predictions are evaluated using first-principles calculations and the accompanying data are used to further refine the ML models. Our ML-accelerated first-principles calculation approach offers more efficient predictions of the crystal structure and mechanical properties for Co-V-Ta- and Co-Al-V-based systems than the traditional counterpart. This approach is applicable to expediting crystal structure and mechanical property calculations and thus the design and discovery of other advanced materials beyond Co-based superalloys.

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