Machine Learning Aided Design of Reversible MXene Electrocatalysts for Li-Air Batteries

Sandeep Das; Souvik Manna; Amitabha Das; Brindaban Modak; Biswarup Pathak

doi:10.1039/d6ta00850j

January 1, 2026Journal of Materials Chemistry A

Machine Learning Aided Design of Reversible MXene Electrocatalysts for Li-Air Batteries

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Key Points

The research aims to enhance the design of reversible electrocatalysts for Li-air batteries through machine learning techniques.
Utilized machine learning algorithms to optimize the design of MXene-based electrocatalysts.
Evaluated the performance of newly designed electrocatalysts in relation to the oxygen reduction reaction kinetics.
Conducted comparative analyses with traditional electrocatalysts.
Identified MXene structures that significantly improve reaction kinetics.
Demonstrated higher electrocatalytic activity compared to existing materials.
Showed potential for better energy efficiency in Li-air batteries.

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Implication

Demonstrates improved kinetics in Li-air batteries using machine learning designed electrocatalysts, indicating greater commercialization potential.

Abstract

Li-air batteries (LABs) are noted for their remarkable potential to deliver high energy density. However, their commercialization faces challenges due to slow kinetics in the oxygen reduction reaction through Li...

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Das et al. (2026) studied this question.

synapsesocial.com/papers/69c37b74b34aaaeb1a67de33 https://doi.org/10.1039/d6ta00850j

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Machine Learning Aided Design of Reversible MXene Electrocatalysts for Li-Air Batteries

Key Points

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Abstract

Discussion

Cite This Study

Machine Learning Aided Design of Reversible MXene Electrocatalysts for Li-Air Batteries

Key Points

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Implication

Abstract

Discussion

Cite This Study

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