Supervised Machine Learning-Based Classification of Li-S Battery Electrolytes

• 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Annan teknik -- Övrig annan teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies -- Other Engineering and Technologies not elsewhere specified (hsv//eng)

NATURVETENSKAP  -- Kemi -- Teoretisk kemi (hsv//swe)

NATURAL SCIENCES  -- Chemical Sciences -- Theoretical Chemistry (hsv//eng)

Keyword

polysulfide

supervised machine learning

solubility

electrolyte design

lithium-sulfur batteries

Publication and Content Type

art (subject category)

ref (subject category)