To advance the development of aqueous zinc-ion batteries (ZIBs), which are considered a promising alternative for sustainable energy storage, it is a crucial step to explore zinc-free metal anodes to prevent zinc-dendrite formation. Titanium disulfide (TiS2) has emerged as a potential anode material for ZIBs, due to its sizeable interlayer spacing and favorable electrochemical properties. However, its stability during battery cycles must be thoroughly investigated to assess its feasibility from a theoretical perspective. Herein, the first-principles calculations are performed to theoretically investigate the stability during the cycles of zinc-intercalated TiS2 (ZnxTiS2). Despite its formation energies lying above the thermodynamic convex hull, ZnxTiS2 demonstrates robust mechanical and dynamical stabilities over the entire range of zinc-ion intercalation, suggesting its ability to maintain structural integrity under cycling conditions of ZIBs. Our evaluation of structural, elastic, electrochemical, and electronic properties reveals significant changes during the zinc-ion intercalated process, such as the reduction of the open-circuit voltage and changes in the interlayer spacing. These findings indicate that while TiS2 shows promise as an anode material from a theoretical aspect, addressing the irreversible structural changes observed experimentally is essential. Our insights into the mechanism of zinc-ion intercalation in TiS2 provide valuable guidance for future design and optimization of zinc-free metal anodes.

The representation of atomic configurations through cluster correlations, along with the cluster expansion approach, has long been used to predict formation energies and determine the thermodynamic stability of alloys. In this work, a comparison is conducted between the traditional cluster expansion method based on density functional theory and other potential machine learning models, including decision tree‐based ensembles and multi‐layer perceptron regression, to explore the alloying behavior of different elements in multi‐component alloys. Specifically, these models are applied to investigate the thermodynamic stability of triple transition‐metal (Ti−Mo−V)3C2${\rm (Ti-Mo-V)}_3{\rm C}_2$ MXenes, a multi‐component alloy in the largest family of 2D materials that are gaining attention for several outstanding properties. The findings reveal the triple transition‐metal ground‐state configurations in this system and demonstrate how the configuration of transition metal atoms (Ti, Mo, and V atoms) influences the formation energy of this alloy. Moreover, the performance of machine learning algorithms in predicting formation energies and identifying ground‐state structures is thoroughly discussed from various aspects.

The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry. In this study, we leverage novel diffusion probabilistic (DP) models to denoise atomic coordinates rather than adopting the standard score matching approach in CDVAE. Our proposed DP-CDVAE model can reconstruct and generate crystal structures whose qualities are statistically comparable to those of the original CDVAE. Furthermore, notably, when comparing the carbon structures generated by the DP-CDVAE model with relaxed structures obtained from density functional theory calculations, we find that the DP-CDVAE generated structures are remarkably closer to their respective ground states. The energy differences between these structures and the true ground states are, on average, 68.1 meV/atom lower than those generated by the original CDVAE. This significant improvement in the energy accuracy highlights the effectiveness of the DP-CDVAE model in generating crystal structures that better represent their ground-state configurations.