Publications (6)
Learning Repetition-Invariant Representations for Polymer Informatics
Yihan Zhu, Gang Liu, Eric Inae +2
Polymers are large macromolecules composed of repeating structural units known as monomers and are widely applied in fields such as energy storage, construction, medicine, and aero…
Semi-Supervised Graph Imbalanced Regression
Gang Liu, Tong Zhao, Eric Inae +2
Data imbalance is easily found in annotated data when the observations of certain continuous label values are difficult to collect for regression tasks. When they come to molecule…
MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning
Yihan Zhu, Gang Liu, Eric Inae +1
Small molecules are essential to drug discovery, and graph-language models hold promise for learning molecular properties and functions from text. However, existing molecule-text d…
Motif-aware Attribute Masking for Molecular Graph Pre-training
Eric Inae, Gang Liu, Meng Jiang
Attribute reconstruction is used to predict node or edge features in the pre-training of graph neural networks. Given a large number of molecules, they learn to capture structural…
Open Polymer Challenge: Post-Competition Report
Gang Liu, Sobin Alosious, Subhamoy Mahajan +9
Machine learning (ML) offers a powerful path toward discovering sustainable polymer materials, but progress has been limited by the lack of large, high-quality, and openly accessib…
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Gang Liu, Eric Inae, Tong Zhao +3
Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and a…