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,

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

, highlighting the importance of accurate datasets.

The approach identifies and classifies errors such as proton omissions, charge imbalances, and crystallographic disorder, aiming to improve the fidelity of crystal structure databases.

This development is crucial for materials discovery, as computational predictions rely on accurate databases.

Artificial intelligence and machine learning are becoming increasingly central to materials research

, but concerns over dataset reliability are growing.

The study serves as a reminder to ensure the accuracy of underlying datasets to prevent compromised simulations and predictions.

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

Chemistry World — 2025-10-20

Neural Network for Crystal Structure Error Classification

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