Neural Network for Crystal Structure Error Classification
A neural network has been trained to classify crystal structure errors in metal–organic framework (MOF) databases.
As noted by Tiffany Rogers, this study highlights that machine learning models are only as good as the data they are trained on.
- The neural network detects and classifies structural errors, including:
- proton omissions
- charge imbalances
- crystallographic disorder
Artificial intelligence and machine learning are becoming increasingly central to materials research, with scientists often turning to such tools to predict properties of new compounds.
The approach could help improve the accuracy of computational predictions used in materials discovery that rely on such databases.
Author's summary: Neural network improves MOF database accuracy.