83 citations · 119 across the 10 of their papers we have counts for
10 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…
Active learning-enabled multi-objective design of thermally conductive and mechanically compliant polymers
Yuhan Liu, Jiaxin Xu, Renzheng Zhang +2
Polymers are attractive in applications like flexible electronics and thermal interface materials due to their mechanical compliance and processability. However, conventional polym…
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…
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…
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…
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…
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…
Machine Learning-Assisted Exploration of Thermally Conductive Polymers Based on High-Throughput Molecular Dynamics Simulations
Ruimin Ma, Hanfeng Zhang, Jiaxin Xu +4
Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in heat transfer applications. With recent progress in material informatics, machin…
Extremely High Thermal Conductivity of Aligned Polyacetylene Predicted using First-Principles-Informed United-Atom Force Field
Teng Zhang, Jiaxin Xu, Tengfei Luo
Molecular simulations of polymer rely on accurate force fields to describe the inter-atomic interactions. In this work, we use first-principles density functional theory (DFT) calc…