ArticleProceedings of the National Academy of Sciences of the United States of America2023
Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Simplex-based model for nanoparticle grain identification in four-dimensional scanning transmission electron microscopy data.Journal of microscopy · 2026Article
- GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures.Journal of the American Chemical Society · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.Scientific reports · 2026Article
- Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data.Journal of applied crystallography · 2026Article
- Machine learning for experimental design of ultrafast electron diffraction.Scientific reports · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.
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Registered trials
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