New Computational Techniques to Develop Efficient Batteries
Lithium-ion batteries, which power mobile phones and laptops, consist of three major components
In a new study, researchers at the Indian Institute of Science (IISc) and their collaborators comprehensively analyzed widely-used computational techniques. They verified their predictions of the migration barrier values against actual data observed in lab measurements. Based on their analysis, the team proposes a set of robust guidelines to help researchers choose the most accurate computational framework for testing materials that can be used to develop highly efficient batteries in the future.
Lithium-ion batteries, which power mobile phones and laptops, consist of three major components: a solid negative electrode (anode), a solid positive electrode (cathode), and a liquid or solid electrolyte that separates them. While charging or discharging, lithium-ions migrate across the electrolyte, creating a potential difference. “The electrodes in lithium-ion batteries are not 100% solid. Think of them like a sponge. They have ‘pores’ through which a lithium ion has to pass,” explains Sai Gautam Gopalakrishnan, Assistant Professor at the Department of Materials Engineering, IISc, and corresponding author of the paper published in npj Computational Materials.
An important parameter determining the rate at which the lithium ions penetrate these pores is the migration barrier – the energy threshold the ions need to overcome to traverse through the electrode. “The lower the migration barrier, the faster you can charge or discharge the battery,” says Reshma Devi, PhD student at the Department of Materials Engineering and the first author of the study.
“The same migration barrier value is calculated by one group using one computational technique and another by using another technique. The values may be equivalent, but we cannot know that for sure,” explains Gopalakrishnan.
Two specific approximations, called Strongly Constrained and Approximately Normed (SCAN) and Generalised Gradient Approximation (GGA), are the most widely used methods to arrive at the migration barrier computationally, but each one has its disadvantages. “We took nine different materials,” Reshma Devi explains. “We checked which approximations come closest to the experimental values for each.”
The team found that the SCAN functional had better numerical accuracy overall, but the GGA calculations were faster. GGA was found to have a reasonable level of accuracy in calculating the migration barrier in specific materials (such as lithium phosphate), and might be a better option if a quick estimation was needed, the researchers suggest.
Such insights can be valuable for scientists who seek to test new materials for their performance before they are adapted for battery-related applications, says Gopalakrishnan. “Suppose you have an unknown material, and if you quickly want to see whether this material is useful in your application, then you can use computations to do that, provided you know which computational approximation gives you the closest values. This is useful when it comes to materials discovery.”
The team is also working on developing machine learning tools that can help speed up predictions of migration barriers for a diverse range of materials. (India Science Wire)
