Enhancing Structure-Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning

Abstract

Porous materials have emerged as promising solutions for a wide range of energy and environmental applications. However, the asymmetric development in the field of metal-organic frameworks (MOFs) has led to a data imbalance when it comes to MOFs versus other porous materials such as covalent organic frameworks (COFs), porous polymer networks (PPNs), and zeolites. To address this issue, we introduce PMTransformer (Porous Material Transformer), a multimodal Transformer model pretrained on a vast data set of 1.9 million hypothetical porous materials, including metal-organic frameworks, covalent organic frameworks, porous polymer networks, 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 the fine-tuning performance with a limited number of examples. As a proof of concept, we demonstrate its effectiveness in predicting band gap values of COFs using the available MOF data in the training set. Moreover, we established cross-material relationships in porous materials by predicting the unseen properties of other classes of porous materials. Our approach presents a new pathway for understanding the underlying relationships among various classes of porous materials, paving the way toward a more comprehensive understanding and design of porous materials.