Enhancing Ion Mobility in Solids with Physics-Informed and AI-Driven Descriptors

Abstract

The field of sustainable energy storage and conversion has witnessed increasing interest in ionic conductors due to their potential applications in electrochemical devices such as electrodes and solid electrolytes. In this study, we employed a combination of density functional theory investigations and artificial intelligence techniques to develop a robust descriptor for ion mobility [1,2]. Our descriptor incorporates factors like structural distortions, electronegativity, ionic radii, and oxidation states. By investigating various charge carrier chemistries and the anion and cation chemistries of the host lattice, we discovered that migration barriers are interconnected through linear scaling relations. Building upon this descriptor, we conducted a comprehensive study that employed both theoretical and experimental approaches to identify cathode materials with enhanced mobility for Mg-ions. Our focus was on oxide spinels, and we employed periodic density functional theory calculations to analyze the migration of Mg2+ ions and evaluate the electronic, geometrical, and stability properties of different transition metals within the spinel structures. Our findings reveal that electronegative transition metals, such as Rh, facilitate high mobility of Mg-ions and promote favorable cathode functionality within oxide spinel frameworks [3]. Based on these insights, we identified MgRh2O4 as a promising candidate and proceeded to prepare and characterize it structurally. This study opens up new possibilities for utilizing functional cathode materials with improved transport properties in battery-related applications. [1] M. Sotoudeh and A. Groß JACS Au 2022, 2, 463–471.[2] M. Sotoudeh, M. Dillenz, A. Groß, Adv. Energy Sustainability Res. 2021, 2, 2100113. [3] M. Sotoudeh, M. Dillenz, J. Döhn, J. Hansen, S. Dsoke, A. Groß, Chem. Mater. 2023, 35, 4786–4797.