activity
20242026
most citedMatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models

5 citations · 8 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning

Yanqiao Zhu, Jingru Gan, Xiaoqi Sun +8

Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions. The vast combinatorial…

cs.AI2025★ 3 cited

Evaluating Large Language Models in Scientific Discovery

Zhangde Song, Jieyu Lu, Yuanqi Du +53

Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…

cs.AI2025

MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science

Junkai Zhang, Jingru Gan, Xiaoxuan Wang +8

Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied. To fill this gap, we introduce MatSc…

cs.CV2025

Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRI

Arvind Murari Vepa, Yannan Yu, Jingru Gan +6

Multiparametric 3D brain MRI (mpMRI) is central to neuroradiology, but producing tumor location, appearance, size, and involvement of critical structures for neurosurgical planning…

physics.optics2025

MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery

Jianpeng Chen, Wangzhi Zhan, Haohui Wang +10

Metamaterials, engineered materials with architected structures across multiple length scales, offer unprecedented and tunable mechanical properties that surpass those of conventio…

cond-mat.mtrl-sci2025★ 5 cited

MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models

Jingru Gan, Peichen Zhong, Yuanqi Du +7

Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Langua…

cs.AI2024

MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design

Jingyuan Qi, Zian Jia, Minqian Liu +15

The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resour…