Neural Network to Improve Crystal Structure Databases
A neural network has been trained to classify crystal structure errors in databases, including those for metal–organic frameworks (MOF).
By Tiffany Rogers, 2025-10-20T11:07:00+01:00
The approach detects and classifies structural errors, such as proton omissions, charge imbalances, and crystallographic disorder, to improve the fidelity of crystal structure databases.
Machine learning models are only as good as the data they are trained on.
This study highlights the importance of reliable datasets, as errors in crystal structure databases can compromise downstream simulations and predictions in materials discovery.
Author's note: AI improves materials research accuracy.
- Neural network detects crystal structure errors
- Improves fidelity of MOF databases
- Boosts accuracy of computational predictions
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