31 citations · 52 across the 11 of their papers we have counts for
11 papers
Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
Fiona Y. Wang, Di Sheng Lee, David L. Kaplan +1
Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to…
Artificial Intelligence for Food Innovation
Bianca Datta, Markus J. Buehler, Yvonne Chow +14
Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical, and fragmented. Artificial…
High-throughput Screening of the Mechanical Properties of Peptide Assemblies
Sarah K. Yorke, Zhenze Yang, Aviad Levin +4
Peptides are recognized for their varied self-assembly behaviors, forming a wide array of structures and geometries, such as spheres, fibers, and hydrogels, each presenting a uniqu…
Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a Language Diffusion Model
Bo Ni, Markus J. Buehler
Proteins are dynamic molecular machines whose biological functions, spanning enzymatic catalysis, signal transduction, and structural adaptation, are intrinsically linked to their…
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…
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…
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…
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Bo Ni, Markus J. Buehler
Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, anal…
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
Markus J. Buehler
Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design and manufacturing, including their capacity to work effectively with b…
Crosslinker energy landscape effects on dynamic mechanical properties of ideal polymer hydrogels
Eesha Khare, Amadeus Alcantara, Nic Lee +2
Reversible crosslinkers can enable several desirable mechanical properties, such as improved toughness and self-healing, when incorporated in polymer networks for bioengineering an…
Screening and understanding Li adsorption on 2-dimensional metallic materials by learning physics
Sheng Gong, Shuo Wang, Taishan Zhu +2
Two-dimensional (2D) materials have received considerable attention as possible electrodes in Li-ion batteries (LIBs), although a deeper understanding of the Li adsorption behavior…