Using machine learning towards enhancement of electrochemical activity in OER/ORR half-reactions of MXene cathode materials for Li-air batteries

Metal-air batteries are the target of the ever-growing interest as considering as the new "lead" technology among the most promising electrochemical energy storage solutions. The projected energy density of lithium-air batteries considered in this study exceeds current commercial lithium-ion batteries by more than three times. In this study, we consider the characteristics of MXenes, 2D layered phases with such attractive characteristics as a high specific surface area with the numerous active reaction centers, mechanical strength, the diverse functional characteristics and the perspectives of scalability of their production, which are of importance for the practical realization of Li-air batteries of different architecture. The formation of the phases of complex content and structure inherent to pseudomorphism at the interface, as it is actual for the objects of our study, allows one to conclude that it is necessary to consider the processes that occur at the interfaces of lithium-air battery cathodes in direct relation to the cathode material used. Machine learning methods were involved in model development for (i) MXenes predicting the electrochemical phase diagrams, Pourbaix diagrams, which circumscribe the stability window of MXenes of certain composition formed with synthesis-defined terminations as a function of pH and USHE for single and double MXenes and (ii) the elastic characteristics of MAX phases, precursors of MXenes, to assess the commensurability of the interface of MXene cathode materials and Li2O2 phase as well as the prospects of using target MXene compositions combined with the solid electrolyte materials of different families for employing in all-solid-state Li-air batteries. The obtained models demonstrate high predictive performance that argue on the possibility to use them for rational screening of new phases with desired functional characteristics.