Toward High‐Performance Electrochemical Energy Storage Systems: A Case Study on Predicting Electrochemical Properties and Inverse Material Design of MXene‐Based Electrode Materials with Automated Machine Learning (AutoML)

Abstract This study highlights the potential of Automated Machine Learning (AutoML) to improve and accelerate the optimization and synthesis processes and facilitate the discovery of materials. Using a Density Functional Theory (DFT)‐simulated dataset of monolayer MXene‐based electrodes, AutoML asse...

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Detalles Bibliográficos

Autores principales:	Berna Alemdag, Görkem Saygili, Matthias Franzreb, Gözde Kabay
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Wiley-VCH 2025-10-01
Colección:	Advanced Electronic Materials
Materias:	feature importance intercalation voltage molecular formula prediction PyCaret theoretical capacity
Acceso en línea:	https://doi.org/10.1002/aelm.202400818
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