Open source computational screening identifies potential solid-state battery materials
Researchers from EPFL and NCCR MARVEL in Switzerland have used computational screening to look for new solid state battery materials and have made the results available in an open source tool.
Leonid Kahle, Aris Marcolongo and Nicola Marzari at the NCCR MARVEL Centre for Computational Design and Discovery of Novel Materials developed a computational framework for predicting the diffusion of Li-ions in solid-state electrolyte materials. They show how to use large-scale computational screening to identify new ceramic compounds for further investigation. Used with novel cathode and anode materials, these could prevent the growth of Lithium metal dendrites that cause safety problems and allows smaller, more powerful batteries.
The researchers expect the data, new methods and analysis techniques to be useful in the ongoing search for novel descriptors of fast Li-ion diffusion in solid state batteries, and have made the first-principles simulations publicly available in an open-source archive on MaterialsCloud.
Synthesizing ionic compounds and measuring ionic conductivity are labour intensive tasks and experimental results can be difficult to interpret. Instead, computational methods are easy to automate and run in parallel. These can be used to efficiently identify materials that merit the hassle and expense of experimental investigation in the search for new solid-state electrolytes.
Current approaches to computational screening of battery materials rely on simulations of the electronic structure to determine the insulating character of a material and on molecular dynamics simulations to predict the Li-ion diffusion coefficients. This means running thousands of calculations and so automation and reproducibility are essential, but these computational methods also need to be inexpensive enough to be run for thousands of materials, yet accurate enough to be predictive. The team showed this in the paper High-throughput computational screening for solid-state Li-ion conductors, screening compounds through several stages to look for new structural families for promising lithium conductors.
Next: looking at 1400 battery materials
The approach was used to screen two repositories of experimental structures, the ICSD and COD, which describe some 1,400 unique crystal structures between them. After identifying electronically insulating systems, the scientists used their pinball model to to identify materials likely to display fast-ionic diffusion.
This pinball model is a framework based on physical observations of how electrons behave in an ionic system which simplifies the modelling of ionic conductors. Over 115 identified structures were then simulated with accurate first-principles molecular dynamics for a total of 45 nanoseconds at high and intermediate temperatures.
The approach resulted in the identification of five solid state battery materials with fast ionic diffusion—some in the range of the well-known superionic conductor Li10GeP2S12—as well as 40 materials that at least showed significant diffusion at 1000 K. Though it is not possible to say whether these latter materials can be considered fast-ion conductors at lower temperatures because of the short time scales of the study, they are promising for more detailed study.
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