Neural network trained to classify crystal structure errors in MOF and other databases

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 noted by Tiffany Rogers, "machine learning models are only as good as the data they are trained on."

Study serves as a reminder that 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.
This could help improve the accuracy of computational predictions used in materials discovery that rely on such databases.

Author's summary: Neural network improves crystal structure database fidelity.

Chemistry World — 2025-10-20