ArticleNature communications2021
Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- AdaptMol: domain adaptation for molecular image recognition with limited supervision.Journal of cheminformatics · 2026Article
- Auto-resolving the atomic structure at van der Waals interfaces using a generative model.Nature communications · 2025Article
- Ordered assemblies of peptide nanoparticles with only positive charge.Nature communications · 2024Article
- Basic Cells Special Features and Their Influence on Global Transport Properties of Long Periodic Structures.Entropy (Basel, Switzerland) · 2024Article
- Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review.Materials (Basel, Switzerland) · 2024Review
- Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials.iScience · 2023Review
- Shared metadata for data-centric materials science.Scientific data · 2023Article
- Prediction and Construction of Energetic Materials Based on Machine Learning Methods.Molecules (Basel, Switzerland) · 2022Review
- Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering.Bioengineering (Basel, Switzerland) · 2022Article
- Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation.Journal of applied crystallography · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.
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Registered trials
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