Abstract
<jats:p>Effective industrial separation processes based on nanoporous materials rely heavily on the kinetic properties of the guest molecules in the porosity, yet computational screening studies in the past have focused mostly on the adsorption (or co-adsorption) thermodynamics. Moreover, traditional computational chemistry methods for predicting diffusion coefficients, such as classical molecular dynamics simulations, are often too computationally demanding to be used in high-throughput screening workflows. This work introduces a deep learning framework that overcomes this bottleneck by leveraging 3D voxelized grids of the guest molecule’s Potential Energy Surface (PES) as direct inputs for a 3D Convolutional Neural Network (CNN). Trained on a large dataset of about 7,000 diffusion coefficients for xenon and krypton in MOFs, the deep-learning predictor shows good accuracy with low computational effort. Unlike conventional machine learning approaches that rely on extracted scalar porosity and energy descriptors of the nanoporous materials, our method captures the spatial variations of the hostguest potential energy landscape, enabling a fast estimation of diffusion coefficients. By demonstrating the predictive power of 3D CNNs for guest diffusion prediction on the CoRE MOF 2024 database, we provide a scalable, physically-informed methodology for the multi-scale screening of materials (experimental and hypothetical) and establish a new open-source benchmark dataset to spur development of the field of transport property prediction.</jats:p>