Space group classification
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Benchmarks
CHILI-100K
CHILI-3K
Most implemented
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
Papers
Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
The recent development of Kolmogorov-Arnold Networks (KANs) has found its application in the field of Graph Neural Networks (GNNs) particularly in molecular data modeling and potential drug discovery. Kolmogorov-Arnold G…
Molecular Property PredictionSpace group classificationDrug DiscoveryCHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small orga…
Atomic number classificationBenchmarkingCrystal system classificationDistance regression+9Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from data…
Space group classificationIdentification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning
The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlati…
ClassificationGeneral ClassificationSpace group classificationFast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallograph…
BIG-bench Machine LearningData AugmentationGeneral ClassificationInterpretable Machine Learning+5Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallograph…
BIG-bench Machine LearningData AugmentationGeneral ClassificationSpace group classification+1