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Three-Dimensional Analysis of Strain

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Updated: Jun 10, 2026

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Published on: October 12, 2019

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

Shisheng Zheng1, Bingxu Wang2, Mengmeng Zhang3

  • 1College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, College of Physical Science and Technology, Xiamen University, Xiamen 361000, China.

Chemical Reviews
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

Topological Data Analysis (TDA) reveals hidden patterns in materials data for structure-property discovery. This approach integrates with machine learning, advancing materials informatics and design.

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A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
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Last Updated: Jun 10, 2026

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Published on: October 12, 2019

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
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A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Published on: June 23, 2023

Area of Science:

  • Materials Science
  • Data Science
  • Computational Materials Science

Background:

  • Data-driven approaches are revolutionizing materials science, emphasizing digital data representation.
  • Complex material systems contain intricate data patterns requiring advanced analytical methods.
  • Discovering structure-property relationships is crucial for materials innovation.

Purpose of the Study:

  • To introduce Topological Data Analysis (TDA) as a robust method for materials data analysis.
  • To review TDA methodologies, including persistent homology and topological learning.
  • To highlight TDA's applications and advantages in materials informatics and discovery.

Main Methods:

  • Mathematical foundations of Topological Data Analysis (TDA).
  • Focus on persistent homology, persistent GLMY homology, and Euler characteristic curves.
  • Integration of TDA with machine learning for topological learning models.

Main Results:

  • TDA offers unique robustness, interpretability, and universality for uncovering patterns in material data.
  • Demonstrated applications of TDA in diverse material systems, molecular science, and biochemistry.
  • Identified advantages of TDA in materials informatics for structure-property relationship discovery.

Conclusions:

  • TDA provides a powerful framework for data-driven materials discovery and design.
  • Topological learning enhances predictive modeling capabilities in materials science.
  • Addressing current challenges will further unlock TDA's potential in advancing materials informatics.