Buehler, Markus J.

Polydopamine and Eumelanin: From Structure–Property Relationships to a Unified Tailoring Strategy

d’Ischia, M., Napolitano, A., Ball, V., Chen, C.-T., & Buehler, M. J. (2014) · Accounts of Chemical Research

melanin and skin pigmentationPolymer Surface Interaction Studies

Cited by 668

Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

Gu, G. X., Chen, C.-T., Richmond, D. J., & Buehler, M. J. (2018) · Materials Horizons

Machine Learning in Materials ScienceAdditive Manufacturing and 3D Printing Technologies

Cited by 556

Excitonic effects from geometric order and disorder explain broadband optical absorption in eumelanin

Chen, C.-T., Chuang, C., Cao, J., Ball, V., Ruch, D., & Buehler, M. J. (2014) · Nature Communications

melanin and skin pigmentationSpectroscopy and Quantum Chemical Studies

Cited by 199

Dynamic pigmentary and structural coloration within cephalopod chromatophore organs

Williams, T. L., Senft, S. L., Yeo, J., Martín-Martínez, F. J., Kuzirian, A. M., Martin, C. A., DiBona, C. W., Chen, C.-T., Dinneen, S. R., Nguyen, H. T., et al. (2019) · Nature Communications

Cephalopods and Marine BiologyPhotonic Crystals and Applications

Cited by 178

Molecular modeling of protein materials: case study of elastin

Tarakanova, A., & Buehler, M. J. (2013) · Modelling and Simulation in Materials Science and Engineering

Protein Structure and DynamicsElasticity and Material Modeling

Cited by 160

Fine-tuning large language models for domain adaptation: exploration of training strategies, scaling, model merging and synergistic capabilities

Lu, W., Luu, R. K., & Buehler, M. J. (2025) · npj Computational Materials

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 126

MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge

Ni, B., & Buehler, M. J. (2024) · Extreme Mechanics Letters

Artificial Intelligence ApplicationsModel Reduction and Neural Networks

Cited by 113

Generative Retrieval-Augmented Ontologic Graph and Multiagent Strategies for Interpretive Large Language Model-Based Materials Design

Buehler, M. J. (2024) · ACS Engineering Au

Machine Learning in Materials ScienceTopic Modeling

Cited by 82

Automating alloy design and discovery with physics-aware multimodal multiagent AI

Ghafarollahi, A., & Buehler, M. J. (2025) · Proceedings of the National Academy of Sciences

Machine Learning in Materials ScienceMaterial Selection and Properties

Cited by 72

Multiscale Modeling of Silk and Silk‐Based Biomaterials—A Review

López Barreiro, D., Yeo, J., Tarakanova, A., Martin‐Martinez, F. J., & Buehler, M. J. (2019) · Macromolecular Bioscience

Silk-based biomaterials and applicationsSilkworms and Sericulture Research

Cited by 65

What's Inside the Box? – Length‐Scales that Govern Fracture Processes of Polymer Fibers

Giesa, T., Pugno, N. M., Wong, J. Y., Kaplan, D. L., & Buehler, M. J. (2014) · Advanced Materials

Calcium Carbonate Crystallization and InhibitionPolymer crystallization and properties

Cited by 47

Evidence of the Most Stretchable Egg Sac Silk Stalk, of the European Spider of the Year Meta menardi

Lepore, E., Marchioro, A., Isaia, M., Buehler, M. J., & Pugno, N. M. (2012) · PLoS ONE

Silk-based biomaterials and applicationsSpider Taxonomy and Behavior Studies

Cited by 39

Rapid and automated alloy design with graph neural network-powered large language model-driven multi-agent AI

Ghafarollahi, A., & Buehler, M. J. (2025) · MRS Bulletin

Machine Learning in Materials ScienceThermodynamic and Structural Properties of Metals and Alloys

Cited by 11