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 m2/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.
@article{arxiv.2310.07671,
title = {Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets},
author = {Flaviu Cipcigan and Jonathan Booth and Rodrigo Neumann Barros Ferreira and Carine Ribeiro dos Santos and Mathias Steiner},
journal= {arXiv preprint arXiv:2310.07671},
year = {2024}
}