83 citations · 203 across the 18 of their papers we have counts for
19 papers
ADEPT-PolyGraphMT: Automated Molecular Simulation and Multi-Task Multi-Fidelity Machine Learning for Polymer Property Generation and Prediction
Sobin Alosious, Yuhan Liu, Jiaxin Xu +4
The discovery of polymers with targeted properties is challenged by the vast chemical design space and the limited availability of consistent, high-quality data across multiple pro…
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…
Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research
Gang Liu, Yihan Zhu, Jie Chen +1
Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face crit…
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…
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…
POINT: A Polymer Informatics Training and Testing Database
Jiaxin Xu, Gang Liu, Ruilan Guo +2
The advancement of polymer informatics has been significantly propelled by the integration of machine learning (ML) techniques, enabling the rapid prediction of polymer properties…
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
Gang Liu, Michael Sun, Wojciech Matusik +2
While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug design. This difficulty st…
Learning Molecular Representation in a Cell
Gang Liu, Srijit Seal, John Arevalo +4
Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, curre…
Superior Polymeric Gas Separation Membrane Designed by Explainable Graph Machine Learning
Jiaxin Xu, Agboola Suleiman, Gang Liu +5
Gas separation using polymer membranes promises to dramatically drive down the energy, carbon, and water intensity of traditional thermally driven separation, but developing the me…
Graph Diffusion Transformers for Multi-Conditional Molecular Generation
Gang Liu, Jiaxin Xu, Tengfei Luo +1
Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecular generation, integra…
Large Language Models on Graphs: A Comprehensive Survey
Bowen Jin, Gang Liu, Chi Han +3
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and n…
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Noah Ziems, Gang Liu, John Flanagan +1
Network intrusion detection (NID) systems which leverage machine learning have been shown to have strong performance in practice when used to detect malicious network traffic. Deci…
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…
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…
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…
On the Relationship Between Counterfactual Explainer and Recommender
Gang Liu, Zhihan Zhang, Zheng Ning +1
Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative…
Graph Rationalization with Environment-based Augmentations
Gang Liu, Tong Zhao, Jiaxin Xu +2
Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizabil…
Graph Data Augmentation for Graph Machine Learning: A Survey
Tong Zhao, Wei Jin, Yozen Liu +5
Data augmentation has recently seen increased interest in graph machine learning given its demonstrated ability to improve model performance and generalization by added training da…
Learning from Counterfactual Links for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang +2
Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the association between observed graph structure and exist…