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

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
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
Series:Journal of Advanced Ceramics
Subjects:
max phases
machine learning (ml)
ti2snn
first principles
self-extrusion
Online Access:https://www.sciopen.com/article/10.26599/JAC.2025.9221050
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