Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy

Leitherer A, Yeo BC, Liebscher CH, Ghiringhelli LM (2023)

Publication Type: Journal article

Publication year: 2023

Journal

npj Computational Materials Nature Research

Book Volume: 9

Article Number: 179

Journal Issue: 1

DOI: 10.1038/s41524-023-01133-1

Abstract

Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials. Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision. Here, we present AI-STEM, an automatic, artificial-intelligence based method, for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy (STEM) images of polycrystalline materials. The method is based on a Bayesian convolutional neural network (BNN) that is trained only on simulated images. AI-STEM automatically and accurately identifies crystal structure, lattice orientation, and location of interface regions in synthetic and experimental images. The model is trained on cubic and hexagonal crystal structures, yielding classifications and uncertainty estimates, while no explicit information on structural patterns at the interfaces is included during training. This work combines principles from probabilistic modeling, deep learning, and information theory, enabling automatic analysis of experimental, atomic-resolution images.

Authors with CRIS profile

Luca Ghiringhelli

Involved external institutions

Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) DE Germany (DE) Pukyong National University (PKNU) / 부경대학교 KR Korea, Republic of (KR) Max-Planck-Institut für Eisenforschung GmbH (MPIE) / Max Planck Institute for Iron Research DE Germany (DE)

How to cite

APA:

Leitherer, A., Yeo, B.C., Liebscher, C.H., & Ghiringhelli, L.M. (2023). Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy. npj Computational Materials, 9(1). https://doi.org/10.1038/s41524-023-01133-1

MLA:

Leitherer, Andreas, et al. "Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy." npj Computational Materials 9.1 (2023).

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