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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Hauptverfasser:	Berna Alemdag, Görkem Saygili, Matthias Franzreb, Gözde Kabay
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Wiley-VCH 2025-10-01
Schriftenreihe:	Advanced Electronic Materials
Schlagworte:	feature importance intercalation voltage molecular formula prediction PyCaret theoretical capacity
Online-Zugang:	https://doi.org/10.1002/aelm.202400818
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