Supervised Machine Learning-Based Classification of Li-S Battery Electrolytes
Artikel i vetenskaplig tidskrift, 2021

Machine learning (ML) approaches have the potential to create a paradigm shift in science, especially for multi-variable problems at different levels. Modern battery R&D is an area intrinsically dependent on proper understanding of many different molecular level phenomena and processes alongside evaluation of application level performance: energy, power, efficiency, life-length, etc. One very promising battery technology is Li-S batteries, but the polysulfide solubility in the electrolyte must be managed. Today, many different electrolyte compositions and concepts are evaluated, but often in a more or less trial-and-error fashion. Herein, we show how supervised ML can be applied to accurately classify different Li-S battery electrolytes a priori based on predicting polysulfide solubility. The developed framework is a combined density functional theory (DFT) and statistical mechanics (COSMO-RS) based quantitative structure-property relationship (QSPR) model which easily can be extended to other battery technologies and electrolyte properties.


supervised machine learning


electrolyte design

lithium-sulfur batteries


Steffen Jeschke

Chalmers, Fysik, Materialfysik

Patrik Johansson

Chalmers, Fysik, Materialfysik

Centre national de la recherche scientifique (CNRS)

Batteries and Supercaps

25666223 (eISSN)

Vol. In Press

High energy lithium sulphur cells and batteries (HELIS)

Europeiska kommissionen (EU) (EC/H2020/666221), 2015-06-01 -- 2019-05-31.


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