PMTransformer: Universal Transfer Learning and Cross-material Few-shot Learning in Porous Materials

27 April 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Porous materials have emerged as a promising solution for a wide range of energy and environmental applications. However, the asymmetric development in the field of MOFs has led to data imbalance when it comes to MOFs versus other porous materials such as COFs, PPNs, and zeolites. To address this issue, we introduce PMTransformer (Porous Material Transformer), a multi-modal pre-trained Transformer model pre-trained on a vast dataset of 1.9 million hypothetical porous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), porous polymer networks (PPNs), and zeolites. PMTransformer showcases remarkable transfer learning capabilities, resulting in state-of-the-art performance in predicting various porous material properties. To address the challenge of asymmetric data aggregation, we propose cross-material few-shot learning, which leverages the synergistic effect among different porous material classes to enhance fine-tuning performance with a limited number of examples. As a proof of concept, we demonstrate its effectiveness in predicting bandgap values of COFs using the available MOF data in the training set. Moreover, we established cross-material relationships in porous materials by predicting unseen properties of other classes of porous materials. Our approach presents a new pathway for understanding the underlying relationships between various classes of porous materials, paving the way toward a more comprehensive understanding and design of porous materials.


porous materials
transfer learning
few-shot learning

Supplementary materials

Supplementary Information
Supplementary Note S1-4, Supplementary Figure S1-11, Supplementary Table S1-7


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