most citedEvaluating Large Language Models in Scientific Discovery

1 citations · 1 across the 2 of their papers we have counts for

collaborators

7 papers

cs.AI20261 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-sci2026

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Aritra Roy, Kevin Shen, Andrew MacBride +350

Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…

cs.LG2025

CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction

Ella Miray Rajaonson, Mahyar Rajabi Kochi, Luis Martin Mejia Mendoza +2

Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of…

cond-mat.mtrl-sci2025

The Rise of Generative AI for Metal-Organic Framework Design and Synthesis

Chenru Duan, Aditya Nandy, Shyam Chand Pal +17

Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laboriou…

cond-mat.mtrl-sci2025

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling

Hendrik Kraß, Ju Huang, Seyed Mohamad Moosavi

Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accu…

cs.LG2025

34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery

Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali +32

Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientifi…