1 citations · 3 across the 10 of their papers we have counts for
10 papers
Learning Materials Properties from Scarce Labels and Unlabeled Crystals
Wentao Li, Yizhe Chen, Jiangjie Qiu +3
Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery. We present SemiMat, a controlled benchmark for s…
CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy
Wentao Li, Jiangjie Qiu, Yijun Li +2
Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly on labeled assays but remain l…
ChemWorld: Programmable Chemical Worlds for Controlled and Replayable Agent Experimentation
Jiangjie Qiu, Yijun Li, Xiaonan Wang
Autonomous chemistry increasingly depends on environments in which agents can repeatedly act, observe, and adapt.Physical laboratories provide essential real-material evidence but…
DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation
Jiangjie Qiu, Yijun Li, Wentao Li +1
Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We introduce DriftingMol, a two…
AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement
Jiangjie Qiu, Wentao Li, Honghao Chen +2
Identifying low-energy adsorption geometries on catalytic surfaces is a practical bottleneck for computational heterogeneous catalysis: the difficulty lies not only in the cost of…
CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery
Hou Hei Lam, Jiangjie Qiu, Xiuyuan Hu +5
Organic photovoltaic (OPV) materials offer a promising path toward sustainable energy generation, but their development is limited by the difficulty of identifying high performance…
Bias as a Virtue: Rethinking Generalization under Distribution Shifts
Ruixuan Chen, Wentao Li, Jiahui Xiao +3
Machine learning models often degrade when deployed on data distributions different from their training data. Challenging conventional validation paradigms, we demonstrate that hig…
MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data
Wentao Li, Yizhe Chen, Jiangjie Qiu +1
Data scarcity and the high cost of annotation have long been persistent challenges in the field of materials science. Inspired by its potential in other fields like computer vision…
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
Jiangjie Qiu, Hou Hei Lam, Xiuyuan Hu +5
Organic photovoltaic (OPV) materials offer a promising avenue toward cost-effective solar energy utilization. However, optimizing donor-acceptor (D-A) combinations to achieve high…
MoMa: A Modular Deep Learning Framework for Material Property Prediction
Botian Wang, Yawen Ouyang, Yaohui Li +8
Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fai…