Discover how advanced catalyst materials enable efficient CO2 hydrogenation, converting greenhouse gases into valuable fuels like methanol and hydrocarbons.

In this article, we propose a novel unsupervised framework termed Heterogeneous Graph neural network with bidirectional encoding representation (HGBER) to learn comprehensive node representations.

To overcome these limitations, researchers developed Graph Attention-aware Fusion Networks (GRAF), a framework designed to transform multiplex heterogeneous networks into unified, interpretable ...

The algorithm proposed by the researchers employs a simple graph representation where nodes ... the understanding and design of heterogeneous catalysts for the development of more sustainable ...

In this study, we propose a method based on heterogeneous network and metapath aggregated graph neural network (MAGNN ... Finally, we take the output of the MAGNN as vector representations of microbe ...

Specifically, we first constructed a miRNA-disease heterogeneous graph. Then, we adopted weighted DeepWalk to learn dense representations of miRNAs and diseases from miRNA-miRNA sub-graph and ...

To discover new catalysts using density functional theory (DFT ... fingerprint enumeration details, and graphical representation of difference between absolute and fractional coordinates (PDF) ...

1c01851. Full details of Labeled Site Crystal Graph used in the paper; summary of DimeNet algorithms; optimized parameters of DimeNet algorithms and discussion of machine learning model validation ...