Abstract
This study focuses on developing a high-performance, stable cathode for calcium-ion batteries (CIBs) using a sodium superionic conductor (NASICON) structure to match the energy density and safety standards of current lithium- and sodium-ion batteries. Given the relatively sparse database of CIB materials compared with their lithium and sodium counterparts, expanding the range of new candidates is essential for developing high-performance batteries. To address this, we employed density functional theory (DFT) calculations, which provide a quantum-mechanical description of the electronic properties of materials, to construct a highly reliable database. To improve the accuracy and efficiency, we integrated machine learning interatomic potential with DFT to stabilize the NASICON-type structures, CaxNaV’yV’’2-yBzP3-zO12, where x = 0.8, 0.5, 0; y = 1, 0.5; z = 0.5, 0; V’ and V’’ are transition metals that support stable doped configurations at the V- and P-sites. From the initial 176 candidates, the top 10 materials that facilitate stable structures were identified based on selection criteria focusing on formation energy < 0 eV/atom, energy above hull = 0 eV/atom, gravimetric capacity ≥ 150 mAh/g, -1% ≤ volume change ≤ 1%, and 3 ≤ average voltage ≤ 4.5 V. This approach advances CIB technology and outlines effective strategies for dopant selection to optimize battery cathodes, configuring a framework for future advancements in battery technology.
Published Version
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