Exécution de notre outil de synthèse sur un nouvel article

Résumé : Despite their promise, the targeted synthesis of novel MXenes remains challenging due to their vast structural and compositional space and the lack of reliable stability descriptors that account for synthesis conditions. To address this gap, we utilized density functional theory (DFT) with the r$^2$SCAN functional to develop a high-fidelity dataset of 1,040 ordered binary and ternary bare delaminated MXenes, including mono-transition metal MXenes, in-plane ordered double-transition metal MXenes, in-plane ordered metal-divacancy MXenes, and out-of-plane ordered double-transition-metal MXenes. For each structure, we calculated formation and decomposition Gibbs energies, decomposition products, layer symmetry groups, and energies above the hull. We find that all MXenes are thermodynamically inaccessible under equilibrium conditions. However, 115 predominantly carbide MXenes become thermodynamically stabilized at elevated temperatures (500--2000 K), and 23 are stabilized under high pressure (10 GPa). Additionally, three MXenes in the Mn--Fe--C chemical space are stabilized within an experimentally feasible range of carbon chemical potentials. These findings reveal stability trends across the MXene space and identify synthesis strategies that can guide experimental efforts. Our dataset provides a robust foundation for developing machine-learned stability descriptors and for guiding experimental efforts toward novel MXene discovery. By identifying synthesizable candidates along with their synthesis conditions, this work bridges a critical gap in data-driven approaches to MXene discovery and lays the foundation for future autonomous experimental workflows.