Screening of potential candidates for solid electrolyte interphase materials for Lithium-ion battery through data-driven approach

Q: What type of study is this?

This is a Quantitative Study study.

Sadhana Barman; Utpal Sarkar

doi:10.1039/d5cp02726h

January 1, 2025Physical Chemistry Chemical Physics

Screening of potential candidates for solid electrolyte interphase materials for Lithium-ion battery through data-driven approach

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Key Points

Machine learning significantly enhances the design of materials for lithium-ion batteries.
A data-driven approach allows for effective screening of solid electrolyte interphase materials.
Predictive algorithms are integral in overcoming design challenges for battery components.
This methodology could revolutionize material selection processes in battery technology.

Implication

Data-driven approach predicts material properties for lithium-ion batteries, highlighting advancements in design methodologies.

Abstract

Material property prediction through machine learning has emerged as a revolutionary approach for diminishing hardships in the design of optimal material for practical application. Here, we performed a machine learning...

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Cite This Study

Barman et al. (2025) studied this question.

synapsesocial.com/papers/68d463e231b076d99fa62f53 https://doi.org/10.1039/d5cp02726h

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Screening of potential candidates for solid electrolyte interphase materials for Lithium-ion battery through data-driven approach

Key Points

Implication

Abstract

Discussion

Cite This Study

Screening of potential candidates for solid electrolyte interphase materials for Lithium-ion battery through data-driven approach

Key Points

Implication

Abstract

Discussion

Cite This Study

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