The relationship between diffusion coefficient and ion conductivity in solid-state electrolytes using feedforward neural networks and symbolic regression
Year: 2025
Type: article
Abstract: Electrolyte materials play a crucial role in enabling ion transport without allowing electron passage, making ionic conductivity and diffusion coefficients key properties for improving technologies such as solid-state batteries. Traditional methods for relating these properties, such as the Nernst-Einstein Equation, fail to apply to solid-state electrolytes due to their assumptions of dilute systems. This study proposes a data-driven approach to bridge this gap, developing two mechanistic models to generate a dataset of 2,000 theoretical materials, focusing on solid-state electrolytes. Feedforward neural networks (FNNs) were employed to predict ionic conductivity from diffusion coefficients, achieving relative error rates below 10% for approximately 95% of cases. Conversely, FNNs also predicted diffusion coefficients from conductivity with 97.45% accuracy, improving to 98.23% when the dissociation reaction was known. Additionally, symbolic regression yielded six mathematical equations, offering a slightly less accurate alternative to the neural networks while substantially easier to implement. This work focused on advancing the ability to predict key electrolyte properties using fewer variables, presenting a practical, experimentally feasible solution for the optimization of electrolyte materials in solid-state devices.
Source: Journal of Power Sources
Authors: Igor Gabriel Ito Iwakiri, João Vítor M.A. Silva, Nuno M. Delgado, Fernando Arrais R.D. Lima, Carine Menezes Rebello, Maurício B. de Souza Jr., Idelfonso B.R. Nogueira
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,230
OpenAlex ID: W7077074965