ArticleScientific reports2020
Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures.Journal of the American Chemical Society · 2026Article
- Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X‑ray Diffraction.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data.Journal of applied crystallography · 2026Article
- On artificial crystal structure generation for solving the phase problem with deep learning.Acta crystallographica. Section A, Foundations and advances · 2026Article
- Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models.Nature materials · 2025Article
- A new benchmark for machine learning applied to powder X-ray diffraction.Scientific data · 2025Article
- Machine learning for experimental design of ultrafast electron diffraction.Scientific reports · 2025Article
- Connecting metal-organic framework synthesis to applications using multimodal machine learning.Nature communications · 2025Article
- End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The seventh blind test of crystal structure prediction: structure generation methods.Acta crystallographica Section B, Structural science, crystal engineering and materials · 2024Article
- LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns.Scientific reports · 2024Article
- Efficiency improvement of spin-resolved ARPES experiments using Gaussian process regression.Scientific reports · 2024Article
- Semantic segmentation in crystal growth process using fake micrograph machine learning.Scientific reports · 2024Article
- Accurate space-group prediction from composition.Journal of applied crystallography · 2024Article
- MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions.Digital discovery · 2024Article
- Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials.Scientific reports · 2024Article
- Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.Chemical science · 2023Review
- Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.Journal of synchrotron radiation · 2023Article
- Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells.RSC advances · 2023Article
- Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.