Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets

Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira, Carine Ribeiro Dos Santos, Mathias B Steiner

Keywords: GFlowNet, materials discovery, carbon capture, reticular materials

TL;DR: We train GFlowNets to discover 15 reticular materials outperforming state-of-art for carbon capture.

Abstract: Artificial intelligence holds promise to improve materials discovery. GFlowNets are an emerging deep learning algorithm with many applications in AI-assisted discovery. 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 hypothetical materials outperforming all materials in CoRE2019.

Submission Track: Original Research

Submission Number: 69