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
machine learning models are only as good as the data they are trained on, this study highlights the importance of data accuracy.
- Proton omissions
- Charge imbalances
- Crystallographic disorder
The approach aims to improve the fidelity of crystal structure databases, which is crucial for materials discovery and computational predictions.
Author's summary: Neural network improves crystal structure database accuracy.