A Cumulative Approach to Crystalline Structure Characterization in Atomistic Simulations

Radhi, Ali; Behdinan, Kamran

doi:10.1021/acs.jpcc.8b02608

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…9 Ackland-Jones analysis assigns the radius information and bond angles to the closed-pack structures by measuring their deviations. 10 The idea of describing topological symmetries of a particle's neighborhood is also utilized in common neighborhood parameter (CNP), 11 cumulative CNP, 12 and polyhedral template matching (PTM). 13 Another approach is to implement an order parameter derived from the location of neighboring particles.…”

Section: ■ Introductionmentioning

confidence: 99%

Lightweight Extendable Stacking Framework for Structure Classification in Atomistic Simulations

Deng,

Wang,

et al. 2023

J. Chem. Theory Comput.

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Identifying an atom's local crystal structure is one crucial step in many atomistic simulation analyses. However, many traditional methods are available to only a few limited types of structures, and their performance often relies on manually determined parameters, which may lead to poor classification results in complex material systems. Machine learning models can enhance accuracy and generalizability, but they typically require large amounts of data and computation. This issue could be more severe for deep-learning-based frameworks, especially when confronted with unfamiliar crystal structures. To address this challenge, we propose a lightweight and extendable stacked structure (LESS) classifier, which adopts bond orientational order parameters as features and assembles several efficient machine learning methods as based models. The LESS classifier can recognize a variety of crystal structures, e.g., amorphous, mono, and binary structures, with over 98.8% accuracy on our validation data set, outperforming many current methods even including some deep-learning methods. Our model can also conduct probabilistic classification that aids in the interpretation of atomic structures in complicated environments such as heterogeneous interfaces. Furthermore, when exposed to a completely unknown crystal structure, the LESS framework can efficiently incorporate this new knowledge with generative sampled data from the current model. Overall, our model exhibits great potential as an accurate and flexible atomic structure identification tool featuring high efficiency in both learning and retraining.

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