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.

翻译：人工智能有望推动材料发现的进步。GFlowNets是一种新兴的深度学习算法，在人工智能辅助发现领域具有广泛应用。通过应用GFlowNets，我们生成了多孔网状材料（如金属有机框架和共价有机框架），用于二氧化碳捕获。我们开发了一个新的Python软件包（matgfn）用于训练和采样GFlowNets，并利用matgfn生成了包含新型多样化网状材料的matgfn-rm数据集，其单位质量比表面积超过5000 m²/g。针对matgfn-rm中排名前100的候选材料，我们计算了单组分和双组分气体吸附等温线。这些候选材料相较于最先进的ARC-MOF数据集具有创新性，且其工作容量在CoRE2019数据集中位居前90%分位。我们发现了15种性能优于CoRE2019中所有材料的候选物。