Gas adsorption meets geometric deep learning: points, set and match

Thanks to their unique properties such as ultra high porosity and surface area, metal-organic frameworks (MOFs) are highly regarded materials for gas adsorption applications. However, their combinatorial nature results in a vast chemical space, precluding its exploration with traditional techniques. Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. As such, the latter can not fully exploit the 3D structural information, potentially resulting in a model of lower quality. In this work, we propose a descriptor-free framework called "AIdsorb", which can directly process raw structural information for predicting gas adsorption properties. To accomplish that, the structure is first treated as a point cloud and then passed to a deep learning algorithm suitable for point cloud analysis. As a proof of concept, AIdsorb is applied for predicting $$ uptake in MOFs, outperforming a conventional pipeline that uses geometric descriptors as input. Additionally, to evaluate the transferability of the proposed framework to different host-guest systems, $$ uptake in COFs is examined. Since AIdsorb bases its roots on raw structural information, its applicability extends to all fields of material science.