Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty

Daniele Lanzoni, Marco Albani, Roberto Bergamaschini, and Francesco Montalenti
Phys. Rev. Materials 6, 103801 – Published 3 October 2022

Abstract

We use a convolutional, recurrent neural network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g., leading to the splitting of high aspect-ratio individual structures). The automatic smart augmentation of the training set and design of a hybrid simulation method are discussed.

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  • Received 17 June 2022
  • Accepted 13 September 2022

DOI:https://doi.org/10.1103/PhysRevMaterials.6.103801

©2022 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Daniele Lanzoni*, Marco Albani, Roberto Bergamaschini, and Francesco Montalenti

  • Materials Science Department, University of Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy

  • *Corresponding author: d.lanzoni@campus.unimib.it

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Vol. 6, Iss. 10 — October 2022

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