RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space

Because of their ultrahigh porosity and tunable chemistry, metal-organic frameworks (MOFs) have emerged as leading candidates for gas adsorption applications. Nevertheless, their combinatorial nature induces a vast chemical space, challenging traditional exploration methods. In recent years, machine learning (ML) predictive models have enabled large-scale screening, but they are typically developed for a single adsorption property. This entails that for a new property one must train a model from scratch, a process that requires large amounts of labeled data that are not always available. In our previous work, we demonstrated that combining the potential energy surface─a 3D energy image of the material─with a convolutional neural network improves sample efficiency compared to conventional ML approaches. Here we extend this framework by introducing multitask and transfer learning to foster generalization across gases and conditions, even in data-scarce scenarios. To this end, we developed RetNeXt, a multitask pretrained model on 3.2 million publicly available adsorption-related data, which can be readily adapted to new domains and adsorption tasks. RetNeXt outperforms conventional single-task transfer approaches and achieves up to a 100-fold increase in sample efficiency compared to training from scratch. As such, it can serve as a foundation for future advances in the data-driven adsorption modeling of MOFs.