Space group classification
5 papers with code • 2 benchmarks • 2 datasets
Predicting the space group of a material/nanomaterial graph.
CHILI-3K
Most implemented papers
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
PV-Lab/AUTO-XRD
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X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
PV-Lab/AUTO-XRD
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npj Computational Materials 2019
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
Identification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning
tiongleslie/crystal-structure-classification
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On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e. g., up to 230 space groups), severely limiting its practical usage.
Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms
However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types.
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1. 2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K).
