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."

The approach detects and classifies structural errors, including proton omissions, charge imbalances, and crystallographic disorder, to improve the fidelity of crystal structure databases.

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.

This development serves as a reminder of the importance of data quality in machine learning models, and could help boost the accuracy of computational predictions used in materials discovery.

Author summary: Neural network improves crystal structure database accuracy.

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Chemistry World Chemistry World — 2025-10-20

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