601 citations · 1.2k across the 11 of their papers we have counts for
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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…
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…
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…
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…
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…