  Artificial intelligence holds promise to improve materials discovery.
GFlowNets are an emerging deep learning algorithm with many applications in
AI-assisted discovery. By using GFlowNets, we generate porous reticular
materials, such as metal organic frameworks and covalent organic frameworks,
for applications in carbon dioxide capture. We introduce a new Python package
(matgfn) to train and sample GFlowNets. We use matgfn to generate the matgfn-rm
dataset of novel and diverse reticular materials with gravimetric surface area
above 5000 m$^2$/g. We calculate single- and two-component gas adsorption
isotherms for the top-100 candidates in matgfn-rm. These candidates are novel
compared to the state-of-art ARC-MOF dataset and rank in the 90th percentile in
terms of working capacity compared to the CoRE2019 dataset. We discover 15
materials outperforming all materials in CoRE2019.
