All-around local structure classification with supervised learning: The example of crystal phases and dislocations in complex oxides ☆

J Furstoss and CR Salazar and P Carrez and P Hirel and J Lam, COMPUTER PHYSICS COMMUNICATIONS, 309, 109480 (2025).

DOI: 10.1016/j.cpc.2024.109480

To accurately identify local structures in atomic-scale simulations of complex materials is crucial for the study of numerous physical phenomena including dynamic plasticity, crystal nucleation and glass formation. In this work, we propose a data-driven method to characterize local atomic environments, and assign them to crystal phases or lattice defects. After constructing a reference database, our approach uses descriptors based on Steinhardt's parameters and a Gaussian mixture model to identify the most probable environment. This approach is validated against several test cases: polymorph identification in alumina, and dislocation and grain boundary analysis in the olivine structure.

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