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

A neural network promises to improve the fidelity of crystal structure databases for metal–organic frameworks (MOF) by detecting and classifying structural errors.¹ The approach, which flags entries with proton omissions, charge imbalances and crystallographic disorder, could help boost the accuracy of computational predictions used in materials discovery that rely on such 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; large crystal structure databases often contain errors that can compromise downstream simulations and predictions.