ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning
Authors: Kewei Zhu, Cameron Wilson, Bartosz Mazur, Yi Li, Ashleigh M. Chester, Peyman Z. Moghadam
Organizations: Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom · Department of Micro, Nano, and Bioprocess Engineering, Faculty of Chemistry, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland · Manufacturing Futures Lab, University College London, 7 Sidings Street, London, E20 2AE, United Kingdom
Systematic chemical names, such as IUPAC-style nomenclature for metal-organic frameworks (MOFs), contain rich structural and compositional information in a standardized textual format. Here we introduce ReadMOF, which is, to our knowledge, the first nomenclature-free machine learning framework that leverages these names to model structure-property relationships without requiring atomic coordinates or connectivity graphs. By employing pretrained language models, ReadMOF converts systematic MOF names from the Cambridge Structural Database (CSD) into vector embeddings that closely represent traditional structure-based descriptors. These embeddings enable applications in materials informatics, including property prediction, similarity retrieval, and clustering, with performance comparable to geometry-dependent methods. When combined with large language models, ReadMOF also establishes chemically meaningful reasoning ability with textual input only. Our results show that structured chemical language, interpreted through modern natural language processing techniques, can provide a scalable, interpretable, and geometry-independent alternative to conventional molecular representations. This approach opens new opportunities for language-driven discovery in materials science.
The Chinese University of Hong Kong, Shenzhen · Shanghai Artificial Intelligence Laboratory · Cheriton School of Computer Science, University of Waterloo