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Neural networks for local structure detection in polymorphic systems.

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Artificial neural networks accurately identify local atomic structures in simulations. This method reliably detects crystalline and amorphous arrangements, outperforming traditional approaches.

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Area of Science:

  • Computational materials science
  • Artificial intelligence in physics
  • Atomistic simulations

Background:

  • Accurate identification of local atomic structures is crucial for atomistic computer simulations.
  • Existing methods for structure detection can be unreliable for complex atomic arrangements.

Purpose of the Study:

  • To demonstrate the capability of artificial neural networks (ANNs) for identifying and classifying local ordered and disordered structures in atomistic simulations.
  • To develop a flexible and reference-frame-independent method for structure recognition.

Main Methods:

  • Utilizing a neural network approach based on symmetry functions that characterize the atomic environment.
  • Training ANNs to recognize local atomic arrangements from these symmetry functions.
  • Applying the method to Lennard-Jones systems, liquid water, and ice.

Main Results:

  • The developed ANN method accurately identifies amorphous and crystalline structures.
  • The approach demonstrates high accuracy even with complex atomic arrangements.
  • The algorithm is simple, flexible, and does not require a defined reference frame.

Conclusions:

  • Properly trained ANNs are effective tools for the accurate identification and classification of local structures in atomistic simulations.
  • The ANN-based method offers a reliable alternative to conventional structure detection techniques, especially for complex systems.