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, "A neural network promises to improve the fidelity of crystal structure databases for metal–organic frameworks (MOF) by detecting and classifying structural errors."
Study serves as a reminder that machine learning models are only as good as the data they are trained on.
The approach flags entries with proton omissions, charge imbalances, and crystallographic disorder, which could help boost the accuracy of computational predictions used in materials discovery.
Author's summary: AI improves crystal structure databases.
- Artificial intelligence and machine learning are central to materials research.
- Large crystal structure databases often contain errors that compromise simulations and predictions.
More News
- Ninja Gaiden 4 Review - Blackened Rain Beckons | TechRaptor
- Ninja Gaiden 4 (PS5) Review: Dream Collab Results in Stellar Action Game - PlayStation LifeStyle
- Ireland Is Failing at Smoking Cessation. Now It Will Tax Vapes Hard.
- Using ‘integration’ to silence culturally specific care - Healthy Debate
- XNA completes solar array, detaches from Highfill - Talk Business & Politics