Neural Network to Classify Crystal Structure Errors
A neural network has been trained to detect and classify structural errors in crystal structure databases for metal–organic frameworks (MOF).
By Tiffany Rogers, 2025-10-20T11:07:00+01:00
The approach identifies entries with proton omissions, charge imbalances, and crystallographic disorder, aiming to improve the accuracy of computational predictions in materials discovery.
Machine learning models are only as good as the data they are trained on.
- Artificial intelligence and machine learning are becoming central to materials research.
- Large crystal structure databases often contain errors that can compromise downstream simulations and predictions.
Author's summary: Neural network improves MOF database accuracy.
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