Neural Network to Improve Crystal Structure Databases
A neural network has been trained to classify crystal structure errors in metal–organic frameworks (MOF) databases.
By Tiffany Rogers, 2025-10-20T11:07:00+01:00.
The approach detects and classifies structural errors, including 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 innovation could boost the accuracy of computational predictions used in materials discovery that rely on such databases, as artificial intelligence and machine learning become increasingly central to materials research.
However, concerns are growing over the reliability of the underlying datasets, with large crystal structure databases often containing errors that can compromise downstream simulations and predictions.
Author's summary: Neural network improves MOF database accuracy.