材料研究中的可解释机器学习

doi:10.11900/0412.1961.2024.00160

金属学报

2024, Vol. 60

Issue (10): 1345-1361 DOI: 10.11900/0412.1961.2024.00160

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材料研究中的可解释机器学习

王冠杰, 刘盛咸, 周健, 孙志梅(

)

北京航空航天大学材料科学与工程学院北京 100191

Explainable Machine Learning in the Research of Materials Science

WANG Guanjie, LIU Shengxian, ZHOU Jian, SUN Zhimei(

)

School of Materials Science and Engineering, Beihang University, Beijing 100191, China

引用本文:

王冠杰, 刘盛咸, 周健, 孙志梅. 材料研究中的可解释机器学习[J]. 金属学报, 2024, 60(10): 1345-1361.
Guanjie WANG, Shengxian LIU, Jian ZHOU, Zhimei SUN. Explainable Machine Learning in the Research of Materials Science[J]. Acta Metall Sin, 2024, 60(10): 1345-1361.

全文: PDF(2959 KB) HTML

摘要:

随着人工智能(AI)技术的迅速发展，机器学习在材料研发与设计中发挥着越来越重要的作用。传统机器学习模型往往为“黑盒”模型，限制了科研人员对模型决策过程的理解和信任。而可解释机器学习(XML)可以揭示机器学习模型的内部机制，提供对模型决策过程的洞察。本文从可解释机器学习的基础知识出发，概述了可解释机器学习方法的发展历程和重要里程碑，以及可解释机器学习在人工智能领域的定位和需要遵守的F.A.S.T.原则；进一步介绍了模型内部结构可解释和外部评估模型可解释的2大类可解释机器学习方法及其在材料学中的应用案例。特别地，本团队提出的可解释符号回归和可视化机器学习方法将为材料研发与设计提供新的工具。最后，展望了可解释机器学习在材料学领域的潜在发展方向。

关键词 ：可解释机器学习, 材料基因工程, 符号回归

Abstract：

With the rapid advancement of artificial intelligence (AI), machine learning is playing an increasingly important role in materials research, development, and design. Traditional machine learning models are often “black box” models that limit researchers' understanding of a model's decision-making and undermines their confidence in the process. Explainable machine learning (XML) can reveal the internal mechanisms of these models and provide insights into their decision-making processes. This study begins with the fundamentals of XML, outlines the development history and notable milestones of XML methods, and discusses the role of XML in AI, emphasizing the Fairness, Accountability, Simplicity, and Transparency (F.A.S.T.) principles that should be followed. Furthermore, this study introduces two major categories of XML methods—those that use model-intrinsic interpretability and those that use external model interpretability—along with their applications in materials science. Specifically, the symbolic regression of XML and visualized XML methods developed by our team offer new tools for materials research and design. Finally, potential directions for XML in the field of materials science are discussed.

Key words： explainable machine learning materials genome engineering symbolic regression

收稿日期: 2024-05-13

ZTFLH:

TB30

基金资助:国家重点研发计划项目(2022YFB3807200)

通讯作者: 孙志梅，zmsun@buaa.edu.cn，主要从事材料基因工程高通量算法、材料数据库、相变存储材料等研究

Corresponding author: SUN Zhimei, professor, Tel: (010)82317747, E-mail: zmsun@buaa.edu.cn

作者简介: 王冠杰，男，1994年生，博士

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https://www.ams.org.cn/CN/10.11900/0412.1961.2024.00160 或 https://www.ams.org.cn/CN/Y2024/V60/I10/1345

图1 可解释机器学习(XML)发展历程(根据Explainable Machine Learning关键词在Web of Science数据库中检索发表的SCI论文数量)

图2 可解释人工智能(XAI)的目标与设想及XML在人工智能(AI)领域中的定位[69]

图3 XML方法分类

图4 连续决策树搜索算法流程，Monte Carlo树搜索(MCTS)决策树结构中根节点、父节点、子节点及其关系的示意图，传统MCTS算法的搜索空间的离散数据点，及本文中的参数搜索问题[83]

图5 K近邻算法在材料分类问题中的训练流程[86]

图6 层级相关性传播(LRP)方法中的反向传播示意图

图7 蛋白质-配体复合物3F7H的三维结构，基于生物学知识的蛋白质-配体相互作用，及PotentialNet模型和InteractionNet模型通过LRP方法获得的原子对解离常数预测贡献的热图[96]

图8 类别激活映射图(CAM)在图片分类问题中的示意图[98]

表 1 XML方法评估结果

图9 基于可解释符号回归方法的二维过渡金属硼化物(MBene)单原子催化剂析氧反应(OER)和氧还原反应(ORR)活性预测模型[103]

图10 ALKEMIE 多尺度高通量计算与数据管理智能平台中的可视化机器学习

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