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Found 2 papers, 1 papers with code
Improving Subgraph Representation Learning via Multi-View Augmentation
Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-property relationships and collective cellular function.
Stacked Ensemble Machine Learning for Range-Separation Parameters
Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the nonempirical but expensive optimally tuned range-separated hybrid (OT-RSH) functionals were developed.
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