Open source computational screening identifies potential solid-state battery materials

January 16, 2020 //By Nick Flaherty
Swiss researchers have increasingly using computational models and machine learning to look for new, more effective, combinations of battery materials.
Swiss researchers have used computational modelling to look for more efficient combinations of lithium ion 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


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