Robust crystal structure identification at extreme conditions using a density-independent spectral descriptor and supervised learning

TL;DR

This paper introduces a density-independent spectral descriptor and a simple supervised learning model for robust crystal structure identification under extreme conditions, enabling real-time analysis in large-scale molecular dynamics simulations.

Contribution

It proposes a novel density-independent spectral descriptor and a lightweight classification model for accurate, transferable crystal structure identification at finite temperatures.

Findings

Model outperforms traditional algorithms like a-CNA, PTM, and IDS in extreme conditions.

Demonstrates effective tracking of phase transformations in Zirconium.

Visualizes dislocation dynamics in Aluminum at high temperature.

Abstract

The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computationally efficient, accurate, and stable at finite temperature to reliably extract the relevant features of the data at simulation time. In this work, we leverage spectral descriptors to encode local atomic environments and build crystal structure classification models. In addition to the classical way spectral descriptors are computed, i.e. over a fixed radius neighborhood sphere around a central atom, we propose an extension to make them independent from the material's density. Models are trained on defect-free crystal structures with moderate thermal noise and elastic deformation, using the linear discriminant analysis (LDA) method for dimensionality reduction and logistic regression (LR) for subsequent classification. The proposed classification model is intentionally designed to be simple, incorporating only a limited number of parameters. This deliberate simplicity enables the model to be trained effectively even when working with small databases. Despite the limited training data, the model still demonstrates inherent transferability, making it applicable to a broader range of scenarios and datasets. The accuracy of our models in extreme conditions is compared to traditional algorithms from the literature, namely adaptive common neighbor analysis (a-CNA), polyhedral template matching (PTM) and diamond structure identification (IDS). Finally, we showcase two applications of our method: tracking a solid-solid BCC-to-HCP phase transformation in Zirconium at high pressure up to high temperature, and visualizing stress-induced dislocation loop expansion in single crystal FCC Aluminum containing a Frank-Read source, at high temperature.