1 citations · 4 across the 11 of their papers we have counts for
12 papers
SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code
Wentao Li, Yibo Wu, Yizhe Chen +5
Scientific figures are the interface through which research claims are inspected and reused, but final published panels rarely expose the data or plotting code that produced them.…
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…
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Tiancheng Li, Wentao Li, Anyang Peng +4
Machine-learning interatomic potentials now approach quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones dep…
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…
A Graph Neural Network for the Era of Large Atomistic Models
Duo Zhang, Anyang Peng, Chun Cai +11
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functio…
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…
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
Yaotian Yang, Yiwen Tang, Yizhe Chen +14
Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-for…
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…