Tree-based interpretable machine learning of the thermodynamic phases

被引：3
|
作者：
Yang, Jintao [1 ,2 ]
Cao, Junpeng [1 ,2 ,3 ,4 ]
机构：
[1] Chinese Acad Sci, Inst Phys, Beijing Natl Lab Condensed Matter Phys, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China
[3] Songshan Lake Mat Lab, Dongguan 523808, Guangdong, Peoples R China
[4] Peng Huanwu Ctr Fundamental Theory, Xian 710127, Peoples R China
来源：
PHYSICS LETTERS A | 2021年 / 412卷
基金：
中国国家自然科学基金;
关键词：
Interpretable machine learning; Thermodynamic phase transition; Extreme gradient boosting;
D O I：
10.1016/j.physleta.2021.127589
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
We study the tree-based interpretable machine learning of the thermodynamic phases in a square lattice model. By this method, the interpretability can be achieved and the precision is very high. We obtain the influence of each input feature and catch the main contribution to thermal equilibrium states, without the prior knowledge of phase classification and transition. The tree-based interpretable machine learning can be used to study the unclear impact of inputs on the physical properties and distinguish the roles of input features playing. (c) 2021 Elsevier B.V. All rights reserved.
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页数：5
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