A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti2SnN

To explore the MAX phase with experimental value over a wider range, a data-driven machine learning (ML) model was trained to rapidly predict the stability of MAX phases via a random forest classifier (RFC), support vector machine (SVM), and gradient boosting tree (GBT), where the deemed significant...

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Bibliographic Details

Published in:	Journal of Advanced Ceramics
Main Authors:	Zhiyao Lu, Yun Fan, Zhaoxu Sun, Xiaodong He, Chuchu Yang, Hang Yin, Jinze Zhang, Guangping Song, Yongting Zheng, Yuelei Bai
Format:	Article
Language:	English
Published:	Tsinghua University Press 2025-04-01
Subjects:	max phases machine learning (ml) ti2snn first principles self-extrusion
Online Access:	https://www.sciopen.com/article/10.26599/JAC.2025.9221050