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A. S. Shamshirgar

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Preprint Aug 2026

Data-driven discovery and rapid, direct synthesis of MXenes

MXenes, two-dimensional transition-metal carbides and nitrides, are typically obtained from MAX phases, yet historical reports suggest a broader, largely unexplored chemical space. Here we combine machine-learning-assisted database mining with experiments to uncover overlooked multilayer (ml) MXenes. Screening of repositories reveals a"Treasure Chest"of 38 previously synthesized but unrecognized ml-MXene candidates. Guided by these findings, we rediscover five MXenes using a rapid, scalable self-propagating high-temperature synthesis that requires no sustained external heating and completes within minutes. Inspired by the identified chemistries, we further realize 11 previously unexplored rare-earth-based M2CT2 MXenes (M= Pr, Nd, Sm, Gd, Tb, Ho, and Tm). Experiments and theory reveal semiconducting behavior and diverse magnetic states across this family. Together, these results expand the MXene family and demonstrate a data-driven strategy for accelerating materials discovery through sustainable methods.

A. S. Shamshirgar, G. R. Portugal, S. Ershadrad et al. · 0 citations
Open access Jul 2026

Machine learning screening the feasibility for self-propagating reactions of the MAX and MAB phases with ab initio dataset

As a rapid, scalable, and eco-friendly synthesis method, the self-propagating high-temperature synthesis (SHS) is widely applied for ceramics, however, whose development is always limited by high experimental costs. To address this challenge, a workflow for its feasibility is established by combining first-principles calculations and machine learning to quickly predict SHS reactions of MAX and MAB phases. Based on feasibility criteria of SHS (adiabatic combustion temperature T ad  > 1800 K), 60 MAX and 19 MAB phases are predicted to be feasible for direct-ignition SHS under ideal adiabatic assumptions, with 17 experimentally validated ones. Furthermore, some high- T ad phases are successfully synthesized by SHS, confirming the practical utility of the calculated thermodynamic properties and T ad . It follows that all the data of T ad as well as elemental properties are fed to train a Random Forest Regression model and a SISSO-derived analytical model. Moreover, the synergistic effect of low-VEC transition metals and high-VEC main-group elements significantly improves the heat release performance. Of much interest, a new MAB phase V 5 PB 2 is experimentally discovered by SHS with the aid of machine-learning models. This screening workflow is expected to be a valuable tool for future large-scale synthesis and optimization of reaction conditions for materials.

H. Yin, Wanjun Yu, Yongdong Yu et al. · 0 citations