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

4 citations · 12 across the 8 of their papers we have counts for

collaborators

8 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…

physics.soc-ph2025

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions

Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…

physics.chem-ph2025

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

Taoyong Cui, Yunhong Han, Haojun Jia +2

Transition state (TS) characterization is central to computational reaction modeling, yet conventional approaches depend on expensive density functional theory (DFT) calculations,…

physics.chem-ph2025★ 1 cited

Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks

Chenru Duan, Aditya Nandy, Sizhan Liu +5

Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force…

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…

physics.chem-ph2024★ 1 cited

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models

Jieyu Lu, Zhangde Song, Qiyuan Zhao +4

Designing functional transition metal complexes (TMCs) faces challenges due to the vast search space of metals and ligands, requiring efficient optimization strategies. Traditional…

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…