31 citations · 52 across the 11 of their papers we have counts for
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cond-mat.soft2024
Learning the rules of peptide self-assembly through data mining with large language models
Zhenze Yang, Sarah K. Yorke, Tuomas P. J. Knowles +1
Peptides are ubiquitous and important biologically derived molecules, that have been found to self-assemble to form a wide array of structures. Extensive research has explored the…
cs.CL2024★ 5 cited
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities
Wei Lu, Rachel K. Luu, Markus J. Buehler
The advancement of Large Language Models (LLMs) for domain applications in fields such as materials science and engineering depends on the development of fine-tuning strategies tha…
cs.LG2024★ 3 cited
Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
Markus J. Buehler
Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1,000 scientific papers into an ontological knowledge graph. Through an in-depth struct…