Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale.

Area of Science:

• Chemistry
• Materials Science
• Computational Science

Background:

• Data-driven research is increasingly important in chemistry and materials science.
• Machine learning (ML) offers powerful tools for analyzing complex datasets.
• Rechargeable batteries are a key area benefiting from advanced computational methods.

Purpose of the Study:

• To review the applications of ML in rechargeable battery research, from microscale to macroscale.
• To highlight ML's role in enhancing theoretical calculations and simulations.
• To discuss ML's potential in extracting insights from experimental and theoretical data.

Main Methods:

• Application of ML to density functional theory (DFT) calculations for new functionals.
• Utilizing ML for molecular dynamics (MD) simulations with new potentials.
• Mining experimental and theoretical datasets to establish structure-function correlations.

Main Results:

• ML enhances descriptions of interfaces and amorphous structures in battery materials.
• Quantitative structure-function relationships enable prediction of ionic conductivity and battery lifespan.
• ML aids in optimizing battery operational strategies, such as fast-charging procedures.

Conclusions:

• ML is a transformative tool for advancing rechargeable battery science.
• Integrating ML with multiscale simulations and experiments will drive future innovations.
• The role of human expertise remains crucial in guiding data-driven battery research.