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,
machine learning models are only as good as the data they are trained on, highlighting the importance of accurate datasets.
The approach detects and classifies structural errors, including proton omissions, charge imbalances, and crystallographic disorder, to improve the fidelity of crystal structure databases.
This can help boost the accuracy of computational predictions used in materials discovery that rely on such databases.
Author's summary: Neural network improves MOF database accuracy.