CrystalX is a deep learning model trained on over 50,000 real X-ray measurements that automates full-atom crystal structure solution, outperforms baselines under temporal validation, and corrects some published structures missed by CheckCIF.
Nature 624(7990), 80–85 (2023)
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A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.
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CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning
CrystalX is a deep learning model trained on over 50,000 real X-ray measurements that automates full-atom crystal structure solution, outperforms baselines under temporal validation, and corrects some published structures missed by CheckCIF.
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Graph neural network for colliding particles with an application to sea ice floe modeling
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.