Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State Electrolytes

Abstract

Na-ion batteries are considered a promising alternative to the analogous Li-ion batteries because of their low manufacturing cost, large abundance, and similar chemical/electrochemical properties. In particular, research on Na-ion solid electrolytes, which resolve the flammability issues associated with liquid electrolytes and increase the energy density obtained using a particular metal anode, is rapidly growing. However, the ionic conductivities of these materials are lower than those of liquids. We present a novel classification approach based on machine learning for identifying Na superionic conductor (NASICON) materials with outstanding ionic conductivities. We obtained new features based on chemical descriptors such as Na content, elemental radii, and electronegativity. We then classified 3573 NASICON structures by implementing the ensemble model of gradient boosting algorithms, with an average prediction accuracy of 84.2%. We further validated the thermodynamic stability and ionic conductivity values of the materials classified as superionic materials by employing density functional theory calculations and ab initio molecular dynamics simulations.

Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes

“…However, CDs are straightforward and generalized to capture and quantify a wide range of chemical properties of materials, allowing for effective application in training the ML model. CDs have been used in battery material research using ML methodologies because of these advantages. , …”

“…CDs have been used in battery material research using ML methodologies because of these advantages. 24,27 Furthermore, because using CDs alone may not fully provide the desired expressiveness, three clear, intuitive, and battery-materialrelevant ADs are added. ADs include gravimetric capacity, degree of ion extraction, and space group number.…”

“…On the other hand, in combination with DFT and ML, it is possible to investigate the extensive exploration space through employing high-throughput computation for the database construction and surrogate model for rapid inference, thereby accelerating the discovery of promising materials. Thus, in the research field of rechargeable batteries, various studies combining ML and DFT are actively progressing, achieving remarkable outcomes: (1) solid-state electrolyte screening, − (2) cathode development, , and (3) anode design are some representative examples.…”