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

Neural Network to Improve Crystal Structure Databases

A neural network has been trained to classify crystal structure errors in metal–organic frameworks (MOF) and other 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 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.

However, concerns are growing over the reliability of the underlying datasets, as large crystal structure databases often contain errors that can compromise downstream simulations and predictions.

Author's summary: Neural network improves crystal structure databases.

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Chemistry World Chemistry World — 2025-10-20

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