MacroConf – dataset & workflows to assess cyclic peptide solution structures

Journal article

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

Authors: Crusius, D; Schnell, JR; Cipcigan, F; Biggin, PC

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Royal Society of Chemistry
• Journal: Digital Discovery
• Volume: 2
• Issue: 4
• Pages: 1163-1177
• Publication date: 2023-08-08
• DOI: 10.1039/d3dd00053b
• EISSN: 2635-098X
• ISSN: 2635-098X
• Language: English
• Keywords: Chemistry; Biological system; Peptide; Cyclic peptide; Workflow; Biology; Database; Computer science; Biochemistry