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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials.

Zhenyao Fang¹, Ting-Wei Hsu¹, Qimin Yan¹

¹Department of Physics, Northeastern University, Boston, Massachusetts 02115, United States.

|October 13, 2025

Summary

This summary is machine-generated.

Machine learning models, using graph neural networks (GNNs), can now efficiently model atomic disorder in materials. This approach reveals how disorder impacts electrical conductivity in MXenes, unlike optical conductivity.

Keywords:

MXene electrical conductivity graph neural network optical conductivity surface termination disorder

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Area of Science:

Materials Science
Computational Materials Science
Condensed Matter Physics

Background:

Atomic disorder significantly impacts material properties like charge transport and catalysis.
First-principles methods struggle to model disorder effects due to computational costs.
Machine learning, especially graph neural networks (GNNs), offers efficient solutions for complex material property predictions.

Purpose of the Study:

To develop a machine learning-assisted framework for modeling thermodynamic and ensemble-averaged properties of disordered materials.
To investigate the influence of atomic disorder on the functional properties of MXene monolayers.

Main Methods:

Integration of equivariant graph neural networks (GNNs) with Monte Carlo simulations.
Development of a general computational framework for disordered materials.
Utilizing surface-termination-disordered MXene (Ti3C2T2-x) as a model system.

Main Results:

Electrical conductivity in Ti3C2T2-x shows a peak near the order-disorder transition due to electron scattering and doping.
Optical conductivity is insensitive to local atomic disorder, reflecting global surface composition.
Demonstrated the framework's capability to statistically model disorder effects.

Conclusions:

Atomic disorder plays a critical role in determining material properties.
The developed machine learning framework provides a powerful tool for studying disordered systems like high-entropy alloys and spin liquids.
Distinguishes between local and global effects of disorder on different material properties.