Neural Network for Crystal Structure Error Classification
A neural network has been trained to classify crystal structure errors in metal–organic framework (MOF) and other databases.
As noted by Tiffany Rogers, a study highlights that machine learning models are only as good as the data they are trained on.
Machine learning models are only as good as the data they are trained on.
The approach detects and classifies structural errors, including proton omissions, charge imbalances, and crystallographic disorder, to improve the fidelity of crystal structure databases.
- Proton omissions
- Charge imbalances
- Crystallographic disorder
This can help boost the accuracy of computational predictions used in materials discovery that rely on such databases.
Author's summary: Neural network improves crystal structure database fidelity.