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This review focuses on the integration of topological data analysis (TDA) with machine learning in materials science. It aims to present both the mathematical foundations and the application landscapes of TDA.

June 11, 2026Chemical Reviews

Topological Data Analysis in Materials Science: Principles, Machine Learning Integration, and Application Landscapes

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This review focuses on the integration of topological data analysis (TDA) with machine learning in materials science. It aims to present both the mathematical foundations and the application landscapes of TDA.
Discusses key methodologies such as persistent homology, persistent GLMY homology, and Euler characteristic curves.
Introduces topological learning by integrating TDA with machine learning for predictive modeling.
Highlights representative applications of TDA across diverse material systems.
Outlines the mathematical foundations and advantages of TDA for uncovering data patterns in complex material systems.
Identifies TDA's unique strengths such as robustness and interpretability in discovering structure-property relationships.
Summarizes critical challenges in implementing TDA for materials informatics.

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Implication

Review demonstrates the application of topological data analysis in materials science, suggesting its potential for advancing discovery and design.

Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/6a2a517c80c8f91e7f39db51 https://doi.org/10.1021/acs.chemrev.5c01098

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Implication

Review demonstrates the application of topological data analysis in materials science, suggesting its potential for advancing discovery and design.

Topological Data Analysis in Materials Science: Principles, Machine Learning Integration, and Application Landscapes

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Discussion

Implication

Cite This Study

Topological Data Analysis in Materials Science: Principles, Machine Learning Integration, and Application Landscapes

Key Points

Discussion

Implication

Cite This Study

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