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

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...

Authors

Sandeep Das (ORCID: https://orcid.org/0000-0003-0745-0170)
Biswarup Pathak (ORCID: https://orcid.org/0000-0002-9972-9947)
Brindaban Modak (ORCID: https://orcid.org/0000-0002-7306-8714)
Amitabha Das
Souvik Manna

Publication Details

Journal: Journal of Materials Chemistry A
Published: 2026-01-01
DOI: https://doi.org/10.1039/d6ta00850j
Primary Topic: Advanced Battery Materials and Technologies
Type: article
Field-Weighted Citation Impact: 0.00

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article

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

Sandeep Das, Biswarup Pathak, Brindaban Modak, Amitabha Das et al.

Jan 1, 2026

Journal of Materials Chemistry A

Advanced Battery Materials and Technologies

article

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

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

January 1, 2026article en

Abstract

Journal of Materials Chemistry A

Affordable and clean energy

Openalex Percentile: Top 12%

Advanced Battery Materials and Technologies

DOI Source OpenAlex

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Machine Learning Aided Design of Reversible MXene Electrocatalysts for Li-Air Batteries — Sandeep Das, Biswarup Pathak, et al. · Journal of Materials Chemistry A (2026) | TGRS Research Map | TGRS