132 citations · 310 across the 40 of their papers we have counts for
14 papers · 1 filter
Rules to Tools: Executable Checks for LLM Agents in Scientific Computing
Jingjie Ning, Guojiang Zhao, Chen Xu +4
Scientific coding agents receive equations, boundary conditions, and output requirements in writing, then must assess the programs they revise. Rules to Tools (R2T) supplies prepar…
MolParser-Mobile: Ultrafast OCSR System for Large-Scale Chemical Literature Mining
Xi Fang, Haocheng Lu, Han Lyu +3
Optical Chemical Structure Recognition (OCSR) is a fundamental component of chemical literature mining, enabling molecular database construction, reaction extraction, and AI-driven…
Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning
Shentong Mo, Guolin Ke
Simulating complex fluid flows requires capturing full equilibrium distributions rather than just mean trajectories, yet high-fidelity solvers remain computationally prohibitive. R…
Auto Research for Materials: Auditable AI-Scientist Workflows with Held-Out Transfer
Jingjie Ning, Xiaochuan Li, Shanshan Zhong +2
Auto Research uses language-model agents to propose, implement, and evaluate machine-learning changes in a closed loop, but is usually judged by its terminal pipeline. A terminal s…
Closed-loop Auto Research for Molecular Property Prediction: Discovering and Certifying Generalizable Improvements
Jingjie Ning, Xiaochuan Li, Ji Zeng +2
Closed-loop Auto Research extends automated machine learning from fixed-dataset fitting to changing the research workflow, with language-model agents editing representations and mo…
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Yutang Ge, Guojiang Zhao, Sihang Li +7
Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tu…
Experimental Powder X-ray Diffraction Crystal Structure Determination with RealPXRD-Solver
Qi Li, Mingyu Guo, Rui Jiao +14
Determining crystal structures from experimental powder X-ray diffraction data remains challenging because peak overlap, preferred orientation, and impurity phases obscure atomic a…
Draft-Refine-Optimize: Self-Evolved Learning for Natural Language to MongoDB Query Generation
Mingwei Ye, Jiaxi Zhuang, Mingjun Xu +3
Natural Language to MongoDB Query Language (NL2MQL) is essential for democratizing access to modern document-centric databases. Unlike Text-to-SQL, NL2MQL faces unique challenges f…
OmniScience: A Large-scale Multi-modal Dataset for Scientific Image Understanding
Haoyi Tao, Chaozheng Huang, Nan Wang +4
Multimodal Large Language Models demonstrate strong performance on natural image understanding, yet exhibit limited capability in interpreting scientific images, including but not…
On the Design of One-step Diffusion via Shortcutting Flow Paths
Haitao Lin, Peiyan Hu, Minsi Ren +5
Recent advances in few-step diffusion models have demonstrated their efficiency and effectiveness by shortcutting the probabilistic paths of diffusion models, especially in trainin…
RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature
Hanzheng Li, Xi Fang, Yixuan Li +9
The integration of Multimodal Large Language Models (MLLMs) into chemistry promises to revolutionize scientific discovery, yet their ability to comprehend the dense, graphical lang…
Innovator-VL: A Multimodal Large Language Model for Scientific Discovery
Zichen Wen, Boxue Yang, Shuang Chen +30
We present Innovator-VL, a scientific multimodal large language model designed to advance understanding and reasoning across diverse scientific domains while maintaining excellent…
Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models
Chengqian Zhang, Duo Zhang, Anyang Peng +7
Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-dist…
From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale
Yongqi Jin, Yecheng Wang, Jun-jie Wang +3
Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet existing machine learning metho…