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Updated: Sep 22, 2025

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

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Published on: September 17, 2017

Unsupervised topological learning for identification of atomic structures.

Sébastien Becker^1,2, Emilie Devijver², Rémi Molinier³

¹University of Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France.

Physical Review. E

|May 20, 2022

Summary

We developed a new unsupervised learning method using topological data analysis (TDA) to understand atomic structures in materials. This approach autonomously identifies atomic clusters, aiding the study of complex material behaviors.

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Area of Science:

Materials Science
Computational Materials Science
Data Science

Background:

Understanding local atomic structures is crucial for predicting material properties.
Current methods often require prior knowledge or extensive parameterization.
Autonomous analysis of atomic-scale phenomena remains a challenge.

Purpose of the Study:

To introduce an unsupervised learning methodology for describing local atomic structures.
To enable autonomous identification of atomic structure clusters without a priori knowledge.
To apply this method to analyze materials in crystalline, liquid, and nucleation states.

Main Methods:

Utilizing descriptors based on topological data analysis (TDA) concepts.
Employing Gaussian mixture models for autonomous cluster identification.
Analyzing atomic positions directly from simulations or experimental data.

Main Results:

Successfully applied the methodology to elemental Zirconium (Zr) in crystalline and liquid states.
Demonstrated effectiveness in analyzing homogeneous nucleation events under deep undercooling.
Validated the autonomous identification of distinct atomic structure clusters.

Conclusions:

The proposed TDA-based unsupervised learning method effectively characterizes local atomic structures.
This approach facilitates deeper and autonomous studies of complex materials phenomena at the atomic scale.
Opens new avenues for materials discovery and understanding dynamic processes.