Combining chemical, geometric, and novel topological features to develop generalizable machine learning models for predicting mechanically stable MOFs
Abstract
We present a generalizable, interpretable ML model to predict MOF bulk modulus using novel topological and chemical features. Trained on diverse data, it enables high-throughput screening of ∼435k MOFs for mechanical stability.
Fields of Study & Subjects
Metal-Organic Frameworks: Synthesis and Applications
Machine Learning in Materials Science
X-ray Diffraction in Crystallography
Publication details
- Journal / source
- Journal of Materials Chemistry A
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 14
- Issue
- 11
- Pages
- 6429–6445
- Publication date
- 1 January 2026
- Language
- EN