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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Detaylı Bibliyografya

Asıl Yazarlar:	Berna Alemdag, Görkem Saygili, Matthias Franzreb, Gözde Kabay
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Wiley-VCH 2025-10-01
Seri Bilgileri:	Advanced Electronic Materials
Konular:	feature importance intercalation voltage molecular formula prediction PyCaret theoretical capacity
Online Erişim:	https://doi.org/10.1002/aelm.202400818
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