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.

According to a study, this approach serves as a reminder that

machine learning models are only as good as the data they are trained on
.

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

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 databases.

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Chemistry World Chemistry World — 2025-10-20

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