Issue 4, 2022

Learning the laws of lithium-ion transport in electrolytes using symbolic regression

Abstract

High-throughput experiments (HTE) enable fast exploration of advanced battery electrolytes over vast compositional spaces. Among the multiple properties considered for optimal electrolyte performance, the conductivity is critical. An analytical expression for ionic transport in electrolytes, accurate for practical compositions and operating conditions, would accelerate the process of (i) co-optimizing conductivity alongside other desirable electrolyte properties, and (ii) learning fundamental physical laws from data, which is one of the paramount goals of scientific big-data analytics. Here, we used symbolic regression with an HTE-acquired dataset of electrolyte conductivity and discovered a simple, accurate, consistent and generalizable expression. Notably, despite emerging from a purely statistical approach, the expression reflects functional aspects from established thermodynamic limiting laws, indicating our model is grounded on the fundamental physical mechanisms underpinning ionic transport. We demonstrate the potential of using machine learning with HTE to find accurate and physically-sound models in complex systems without established physico-chemical theories.

Graphical abstract: Learning the laws of lithium-ion transport in electrolytes using symbolic regression

Supplementary files

Transparent peer review

To support increased transparency, we offer authors the option to publish the peer review history alongside their article.

View this article’s peer review history

Article information

Article type
Paper
Submitted
01 4 2022
Accepted
06 6 2022
First published
07 6 2022
This article is Open Access
Creative Commons BY license

Digital Discovery, 2022,1, 440-447

Learning the laws of lithium-ion transport in electrolytes using symbolic regression

E. Flores, C. Wölke, P. Yan, M. Winter, T. Vegge, I. Cekic-Laskovic and A. Bhowmik, Digital Discovery, 2022, 1, 440 DOI: 10.1039/D2DD00027J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements