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

5 citations · 17 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2025

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents

Yuanqi Du, Botao Yu, Tianyu Liu +25

There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…

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…

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.LG2025★ 3 cited

AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential

Bangchen Yin, Jiaao Wang, Weitao Du +9

Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we…

cs.LG2024★ 2 cited

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

Yuanqi Du, Michael Plainer, Rob Brekelmans +5

Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large sp…

physics.chem-ph2024★ 4 cited

React-OT: Optimal Transport for Generating Transition State in Chemical Reactions

Chenru Duan, Guan-Horng Liu, Yuanqi Du +6

Transition states (TSs) are transient structures that are key in understanding reaction mechanisms and designing catalysts but challenging to be captured in experiments. Alternativ…

cs.LG2024

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

Lingkai Kong, Yuanqi Du, Wenhao Mu +8

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed…