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Coarse-Graining and Forecasting Atomic Material Simulations with Descriptors.

Thomas D Swinburne¹

¹Aix-Marseille Université, CNRS, CINaM UMR 7325, Campus de Luminy, 13288 Marseille, France.

Physical Review Letters

|December 22, 2023

Summary

Descriptor functions offer a new way to analyze atomic structures in materials simulations. This method enables accurate property prediction and forecasting, improving the efficiency of large-scale simulations.

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Area of Science:

Computational Materials Science
Materials Informatics
Atomistic Simulations

Background:

Large-scale atomic simulations are resource-intensive for data generation, storage, and analysis.
Predicting material properties and behaviors, such as yielding, requires robust analytical tools.

Purpose of the Study:

To introduce descriptor functions as a general, metric latent space for atomic structures.
To enable efficient analysis and prediction of properties in large-scale materials simulations.
To develop a method for assessing forecast confidence in trajectory predictions.

Main Methods:

Development and application of descriptor functions for atomic structures.
Utilizing a vector autoregressive model for trajectory generation, resampling, and forecasting.
Employing Mahalanobis outlier distance for forecast confidence assessment.

Main Results:

Descriptor functions can regress diverse material properties, including dislocation densities and stress states.
Vector autoregressive models accurately forecast material trajectories and allow property distribution smoothing.
Forecast confidence derived from Mahalanobis distance effectively assesses coarse-grained models.

Conclusions:

Descriptor functions provide a powerful framework for analyzing atomic structures in large-scale simulations.
The proposed forecasting method offers reliable predictions with quantifiable confidence.
Material yielding is linked to a reduction in the intrinsic dimension of the descriptor manifold.