MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal–Organic Frameworks

The computational discovery and design of new crystalline
materials,
particularly metal–organic frameworks (MOFs), heavily rely
on high-quality, “computation-ready” structural data.
However, recent studies have revealed significant error rates within
existing MOF databases, posing a critical data problem that hinders
efficient high-throughput computational screening. While rule-based
algorithms like MOSAEC, MOFChecker, and the Chen and Manz method (Chen–Manz)
have been developed to address this, they often suffer from inherent
limitations and misclassification of structures. To overcome this
challenge, we developed MOFClassifier, a novel machine learning approach
built upon a positive-unlabeled crystal graph convolutional neural
network (PU-CGCNN) model. MOFClassifier learns intricate patterns
from perfect crystal structures to predict a “crystal-likeness
score” (CLscore), effectively classifying MOFs as computation-ready.
Our model achieves an ROC value of 0.979 (previous best value 0.912)
and, importantly, can identify subtle structural and chemical errors
that are undetectable by current rule-based methods. By accurately
recovering previously misclassified false-negative structures, the
MOFClassifier reduces the risk of overlooking promising material candidates
in large-scale computational screening campaigns. This user-friendly
tool is freely available and has been integrated into the preparation
workflow for the updated CoRE MOF DB 2025 v1.0, contributing to the
accelerated computational discovery of MOF materials.