Title: From atom to space: A region-based readout function for spatial properties of materials
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: porous material, graph neural network

Abstract: The message passing–readout framework has become the de facto standard for material property prediction. However, most existing readout functions are built on an atom-decomposable inductive bias, i.e. the material-level property or feature can be reasonably assigned to contributions of individual atoms. This is a strong bias and may not hold for all properties, limiting the application scenarios. In this work, we propose a region-based decomposition perspective, reformulating material properties as integrals over space and pooling contributions from spatial regions rather than atoms. Specifically, we propose a novel readout function named SpatialRead. SpatialRead introduces additional spatial nodes to represent a voxelized space, transforming the atomic isomorphic graph into a heterogeneous atom–space graph with unidirectional message flow from atoms to spatial nodes. To combine the two types of inductive bias, multimodal methods can be used to fuse the feature of atoms the spatial nodes. Such region-based readout function is especially suited for spatial properties such as gas adsorption capacity, separation ratio. Extensive experiments demonstrate that a simple PaiNN–Transformer-based SpatialRead training from scratch outperforms state-of-the-art pre-trained foundation models on these special tasks. Our results highlight the importance of designing physically grounded readout functions tailored to the target property. The code can be found in anonymous github https://anonymous.4open.science/r/SpatialRead-8E92 and dataset will be released after the double-blind review.

TL;DR: We convert the atom-decomposable inductive bias of MPNNs to a region-based one, broadening their applicability to spatial material properties.