Neural Network for Crystal Structure Error Classification
A neural network has been trained to classify crystal structure errors in metal–organic frameworks (MOF) and other databases.
As noted by Tiffany Rogers, "A neural network promises to improve the fidelity of crystal structure databases for metal–organic frameworks (MOF) by detecting and classifying structural errors."
The approach identifies errors such as proton omissions, charge imbalances, and crystallographic disorder, which can help improve the accuracy of computational predictions used in materials discovery.
Study serves as a reminder that machine learning models are only as good as the data they are trained on.
Author's summary: Neural network improves crystal structure database accuracy.
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