Markus Buehler

Research Interests

• Deformation and failure
• Protein folding and property prediction
• Biomass conversion
• Multiscale modeling
• Artificial intelligence, machine learning 
• Additive manufacturing
• Hierarchical materials
• Bio-inspired materials (analysis, design, manufacturing)
• Fracture mechanics
• Solid mechanics
• Deformation and failure
• Rare events
• Molecular modeling methods
• Biophysics
• Category theory
• Deep learning 

Online Data

Additional links and social media

• Google Scholar
• Mastodon
• Twitter 
• LinkedIn

Honors + Awards

• Fellow, Royal Society of Chemistry, 2026
• Gebhardt Distinguished Lectureship, 2026
• Washington Award, 2025
• Member, National Academy of Engineering, 2023
• Society of Engineering Science (SES) James R. Rice Medal, 2022
• International Association for Computational Mechanics (IACM) Fellows Award, 2022
• ASME Drucker Medal, 2021
• Royal Society of Chemistry Materials Horizons Outstanding Paper Prize, 2019
• MIT Distinguished Service and Leadership Award, 2021
• Clarivate Analytics Highly Cited Researcher Award, 2018 (recognized for exceptional research performance demonstrated by production of multiple highly cited papers that rank in the top 1% by citations for field and year in Web of Science)
• Feynman Prize (Foresight Institute), Theory, 2016
• Outstanding Young Scientist Award, NANOSMAT Society, 2016
• Fellow, NANOSMAT Society, 2016
• International Journal of Applied Mechanics (IJAM) Most Cited Paper Award (2009-2015), 2016
• Fellow, American Institute for Medical and Biological Engineering (AIMBE), 2015
• ASME Journal of Applied Mechanics Award 2014 (with student Zhao Qin)
• The Minerals, Metals & Materials Society (TMS) Robert Lansing Hardy Award, 2013
• NAE Frontiers of Engineering: Plenary Speaker, 2008 and 2013; Invited Participant, 2007
• TMS Structural Materials Division Best Paper Award 2013
• Materials Research Society (MRS) Outstanding Young Investigator Award, 2012
• IEEE Holm Conference Morton Antler Lecture Award, 2012
• SES Young Investigator Medal, 2012
• Alfred Noble Prize, 2011
• ASME Thomas J.R. Hughes Young Investigator Award, 2011
• ASCE Leonardo Da Vinci Award, 2011
• Stephen Brunauer Award, 2011 (American Ceramic Society)
• AIME Rossiter W. Raymond Memorial Award, 2011
• ASME Sia Nemat Nasser Award, 2010
• MIT Harold E. Edgerton Faculty Achievement Award, 2010
• Presidential Early Career Award for Scientists and Engineers (PECASE), 2009 (the award was presented by President Barack Obama at the White House)
• United States Navy Young Investigator Award, 2008
• DARPA Young Faculty Award, 2008
• Air Force Office of Scientific Research Young Investigator Award, 2008
• National Science Foundation CAREER Award, 2007
• Materials Research Society Gold Graduate Student Award, 2004

Memberships

• ASME
• Society of Engineering Science 
• Materials Research Society 
• American Institute for Medical and Biological Engineering 

Professional Service

Editorial Activities:

Editor-in-Chief, J. Mech. Behav. Biomed. Mat. (Elsevier); Section Editor, MRS Bulletin Impact; Editor-in- Chief, BioNanoScience (Springer); Cell Matter, Member of the Editorial Advisory Board (Cell Press); Editorial Advisory Board, ACS Biomaterials Science and Engineering (American Chemical Society); Proceedings of the National Academy of Sciences (PNAS), Handling Editor; Editorial Board Member, Extreme Mechanics Letters (Elsevier); Editor Board Member, Scientific Reports (Nature Publishing Group); Editorial Board, Computational Materials Science (Elsevier); Academic Editor, PLoS ONE (Public Library of Science); Associate Editorial Board, Frontiers in Mechanics of Materials (Frontiers); Guest Editor, MRS Bulletin (MRS); Executive Editor, International Journal of Applied Mechanics (Imperial College Press); Associate Editor, Journal of Engineering Mechanics (ASCE); Editorial Board, Journal of Nanomechanics and Micromechanics (ASCE); Associate Editor, J. Comp. Theor. Nanosci. (Amer. Sci. Publ.); Editor, Acta Mech. Sinica (Springer Nature); Guest Editor of J. Mater. Res. (Springer Nature).

Committees and Service (selection):

MIT Committee on Arts, Culture, and DEI, 2021-2022; Chair, MRS Fall 2021 Meeting, 2019-2021; Member, Core Committee of New Engineering Education Transformation (NEET), MIT, 2017-2019; MIT Refugee ACTion (ReACT) Senior Advisory Committee, 2017- now; MIT Center for Computational Engineering Advisory Council, 2019-now; 2018-19, Co-Chair Eighth International Conference on Mechanics of Biomaterials & Tissues; Member of the Executive Committee, ASME Materials Division (2015-2018); Co-Chair, NanoEngineering in Medicine and Biology (NEMB) Congress 2013, Boston, 2013; Chair, Fourth International Conference on Mechanics of Biomaterials & Tissues 2011, Hawai’i; Inaugural Chair, Biomechanics Committee at the ASCE Engineering Mechanics Institute (EMI), 2008-2014; Co-Chair, NanoEngineering for Medicine & Biology Congress Steering Committee of ASME, 2010-2013; Member, ASME Nanoengineering Council Executive Committee, 2010.

Teaching

• 3.021J Introduction to Modeling and Simulation
• 1.050 Engineering Mechanics (undergraduate)
• 2.094/1.545J Atomistic Modeling and Simulation of Materials and Structures
• 2.174/1.052/1.121J Advancing Mechanics and Materials via Machine Learning
• Professional Education: Predictive Multiscale Materials Design 
• Professional Education: Machine Learning for Materials Informatics
• Co-instructor, “Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI,” MIT Professional & Executive Learning

Publications

Selected publications (full list: https://scholar.google.com/citations?user=hWBTSksAAAAJ&hl=en)

A. Agentic AI, graph reasoning, and scientific discovery

• Ghafarollahi, A.; Buehler, M. J. "ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning." Digital Discovery 3, 1389-1409 (2024). 
• Ghafarollahi, A.; Buehler, M. J. "Automating alloy design and discovery with physics-aware multimodal multiagent AI." Proceedings of the National Academy of Sciences 122(4), e2414074122 (2025).
• Buehler, M. J. "MechGPT: A language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines, and modalities." Applied Mechanics Reviews 76(2), 021001 (2024). 
• Ghafarollahi, A.; Buehler, M. J. "SciAgents: Automating scientific discovery through bioinspired multi-agent intelligent graph reasoning." Advanced Materials 37(22), 2413523 (2025).
• Buehler, M. J. "Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning." Machine Learning: Science and Technology 5(3), 035083 (2024).
• Ni, B.; Buehler, M. J. "MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge." Extreme Mechanics Letters 67, 102131 (2024). 
• Buehler, M. J. "PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking." npj Artificial Intelligence 1, Article 4 (2025).
• Buehler, M. J. "In Situ Graph Reasoning and Knowledge Expansion Using Graph-PRefLexOR." Advanced Intelligent Discovery 1(3), e202500006 (2025).
• Buehler, M. J. "Agentic deep graph reasoning yields self-organizing knowledge networks." Journal of Materials Research 40(15), 2204-2242 (2025).
• Buehler, M. J. "Self-organizing graph reasoning evolves into a critical state for continuous discovery through structural-semantic dynamics." Chaos: An Interdisciplinary Journal of Nonlinear Science 35, 113117 (2025).
• Wang, F. Y.; Buehler, M. J. "Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence." arXiv:2606.01444 (2026).
• Lew, A. J.; Cao, Y.; Buehler, M. J. "ProjectionBench: Evaluating Scientific Hypothesis Generation in LLMs Under Progressive Information Disclosure." arXiv:2605.30284 (2026).
• Wong, F. Y.; Buehler, M. J. "Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence." arXiv:2605.22300 (2026).
• Stewart, I. A.; Hage, T. P.; Hsu, Y. C.; Buehler, M. J. "Graphagents: Knowledge graph-guided agentic AI for cross-domain materials design." arXiv:2602.07491 (2026).
• Wang, F. Y.; Marom, L.; Pal, S.; Luu, R. K.; Lu, W.; Berkovich, J. A.; Buehler, M. J. "Autonomous agents coordinating distributed discovery through emergent artifact exchange." arXiv:2603.14312 (2026).
• Hage, T. P.; Buehler, M. J. "BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoning." arXiv:2603.04124 (2026).
• Wang, F. Y.; Lee, D. S.; Kaplan, D. L.; Buehler, M. J. "Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation." arXiv:2511.22311 (2025).

B. Generative and inverse design of materials and proteins

• Gu, G. X.; Chen, C.-T.; Buehler, M. J. "De novo composite design based on machine learning algorithm." Extreme Mechanics Letters 18, 19-28 (2018). [Clarivate Highly Cited]
• Gu, G. X.; Chen, C.-T.; Richmond, D. J.; Buehler, M. J. "Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment." Materials Horizons 5(5), 939-945 (2018). 
• Yang, Z.; Buehler, M. J. "Words to Matter: De novo Architected Materials Design Using Transformer Neural Netwocrks." Frontiers in Materials 8, 740754 (2021).
• Hsu, Y. C.; Yang, Z.; Buehler, M. J. "Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input." APL Materials 10(4) (2022).
• Shen, S. C.-Y.; Buehler, M. J. "Nature-inspired architected materials using unsupervised deep learning." Communications Engineering 1, Article 37 (2022).
• Lew, A. J.; Buehler, M. J. "Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model." Materials Today 64, 10-20 (2023).
• Lu, W.; Lee, N. A.; Buehler, M. J. "Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing." Proceedings of the National Academy of Sciences 120(31), e2305273120 (2023).
• Ni, B.; Kaplan, D. L.; Buehler, M. J. "Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model." Chem 9(7), 1828-1849 (2023).
• Buehler, M. J. “Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design.” Advanced Functional Materials 34, 2409531 (2024).
• Ni, B.; Kaplan, D. L.; Buehler, M. J. "ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model." Science Advances 10(6), eadl4000 (2024).
• Luu, R. K.; Buehler, M. J. “BioinspiredLLM: Conversational large language model for the mechanics of biological and bio-inspired materials.” Advanced Science 11, 2306724 (2024).
• Buehler, E. L.; Buehler, M. J. “X-LoRA: Mixture of low-rank adapter experts, a flexible framework for large language models with applications in protein mechanics and molecular design.” APL Machine Learning 2, 026119 (2024).
• Lu, W.; Kaplan, D. L.; Buehler, M. J. "Generative modeling, design, and analysis of spider silk protein sequences for enhanced mechanical properties." Advanced Functional Materials 34(11), 2311324 (2024).
• Lu, W.; Luu, R. K.; Buehler, M. J. “Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities.” npj Computational Materials 11, 84 (2025).
• Ni, B.; Buehler, M. J. "VibeGen: Agentic end-to-end de novo protein design for tailored dynamics using a language diffusion model." Matter 9, 102706 (2026).
• Yang, Z.; Yorke, S. K.; Knowles, T. P. J.; Buehler, M. J. "Learning the rules of peptide self-assembly through data mining with large language models." Science Advances 11(13), eadv1971 (2025).
• Luu, R. K.; Buehler, M. J. "Bioinspired123D: generative 3D modeling system for bioinspired structures." AI for Science, DOI: 10.1088/3050-287X/ae61d1 (2026).
• Ha, D. Q.; Alam, M. F.; Buehler, M. J.; Ahmed, F.; Carstensen, J. V. "AI-Guided Human-In-the-Loop Inverse Design of High Performance Engineering Structures." arXiv:2601.10859 (2026).

C. Physics-aware surrogate models for mechanics and fields

• Yang, Z.; Yu, C.-H.; Buehler, M. J. "Deep learning model to predict complex stress and strain fields in hierarchical composites." Science Advances 7(15), eabd7416 (2021). [Clarivate Highly Cited]
• Yang, Z.; Yu, C.-H.; Guo, K.; Buehler, M. J. "End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures." Journal of the Mechanics and Physics of Solids 154, 104506 (2021).
• Buehler, M. J. "FieldPerceiver: Domain-agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs." Materials Today 57, 9-25 (2022).
• Lew, A. J.; Buehler, M. J. "DeepBuckle: Extracting physical behavior directly from empirical observation for a material-agnostic approach to analyze and predict buckling." Journal of the Mechanics and Physics of Solids 164, 104909 (2022).
• Buehler, E. L.; Buehler, M. J. "End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks." Biomedical Engineering Advances 4, 100038 (2022).
• Buehler, M. J. "Prediction of atomic stress fields using cycle-consistent adversarial neural networks based on unpaired and unmatched sparse datasets." Materials Advances 3(15), 6280-6290 (2022).
• Buehler, M. J. "Modeling atomistic dynamic fracture mechanisms using a progressive transformer diffusion model." Journal of Applied Mechanics 89(12), 121009 (2022).
• Buehler, M. J. "Predicting mechanical fields near cracks using a progressive transformer diffusion model and exploration of generalization capacity." Journal of Materials Research 38(5), 1317-1331 (2023).
• Hsu, Y. C.; Buehler, M. J. "DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network." APL Machine Learning 1(2) (2023).
• Lew, A. J.; Yu, C.-H.; Hsu, Y. C.; Buehler, M. J. "Deep learning model to predict fracture mechanisms of graphene." npj 2D Materials and Applications 5, 48 (2021).
• Liu, F.; Ni, B.; Buehler, M. J. "PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model." Extreme Mechanics Letters, paper #101803 (2022).
• Khare, E.; Gonzalez-Obeso, C.; Kaplan, D. L.; Buehler, M. J. "CollagenTransformer: End-to-end transformer model to predict thermal stability of collagen triple helices using an NLP approach." ACS Biomaterials Science & Engineering 8(10), 4301-4310 (2022).
• Yang, Z.; Buehler, M. J. "Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks." npj Computational Materials 8, 198 (2022).
• Yang, H.; Meyer, F.; Huang, S.; Yang, L.; Lungu, C.; Olayioye, M. A.; Buehler, M. J.; Guo, M. "Learning collective cell migratory dynamics from a static snapshot with graph neural networks." PRX Life 2(4), 043010 (2024).
• Yang, H.; Roy, G.; Nguyen, A. Q.; Bi, D.; Stern, T.; Buehler, M. J.; Guo, M. "Multicell: geometric learning in multicellular development." Nature Methods 23(3), 617-625 (2026).
• Berkovich, J. A.; David, N. S.; Buehler, M. J. "AutomataGPT: Transformer-based forecasting and ruleset inference for two-dimensional cellular automata." Advanced Science 13, 2511352 (2026)

D. Foundational multiscale mechanics and biomaterials

• Buehler, M. J.; Abraham, F. F.; Gao, H. "Hyperelasticity governs dynamic fracture at a critical length scale." Nature 426, 141-146 (2003).
• Buehler, M. J.; Gao, H. "Dynamical fracture instabilities due to local hyperelasticity at crack tips." Nature 439(7074), 307-310 (2006). 
• Buehler, M. J. "Nature designs tough collagen: Explaining the nanostructure of collagen fibrils." Proceedings of the National Academy of Sciences 103(33), 12285-12290 (2006).
• Buehler, M. J.; Keten, S.; Ackbarow, T. "Theoretical and computational hierarchical nanomechanics of protein materials: deformation and fracture." Progress in Materials Science 53(8), 1101-1241 (2008).
• Buehler, M. J.; Yung, Y. C. "Deformation and failure of protein materials in physiologically extreme conditions and disease." Nature Materials 8(3), 175-188 (2009).
• Keten, S.; Xu, Z.; Ihle, B.; Buehler, M. J. "Nanoconfinement controls stiffness, strength and mechanical toughness of beta-sheet crystals in silk." Nature Materials 9, 359-367 (2010). 
• Gautieri, A.; Vesentini, S.; Redaelli, A.; Buehler, M. J. "Hierarchical structure and nanomechanics of collagen microfibrils from the atomistic scale up." Nano Letters 11(2), 757-766 (2011).
• Cranford, S. W.; Tarakanova, A.; Pugno, N. M.; Buehler, M. J. "Nonlinear material behaviour of spider silk yields robust webs." Nature 482(7383), 72-76 (2012).
• Nair, A. K.; Gautieri, A.; Chang, S. W.; Buehler, M. J. "Molecular mechanics of mineralized collagen fibrils in bone." Nature Communications 4, 1724 (2013).
• Barthelat, F.; Yin, Z.; Buehler, M. J. "Structure and mechanics of interfaces in biological materials." Nature Reviews Materials 1, 16007 (2016).
• Buehler, M. J.; Tang, H.; van Duin, A. C. T.; Goddard, W. A. "Threshold crack speed controls dynamical fracture of silicon single crystals." Physical Review Letters 99, 165502 (2007).
• Pellenq, R. J.-M.; Kushima, A.; Shahsavari, R.; Van Vliet, K. J.; Buehler, M. J.; Yip, S.; Ulm, F.-J. "A realistic molecular model of cement hydrates." Proceedings of the National Academy of Sciences 106(38), 16102-16107 (2009).
• D.I. Spivak, T. Giesa, E. Wood and M.J. Buehler, “Category theoretic analysis of hierarchical protein materials and social networks,” PLoS ONE 6, e23911, 2011.
• T. Giesa, D.I. Spivak and M.J. Buehler, “Category theory-based solution for the building-block replacement problem in materials design,” Advanced Engineering Materials 14, 810–817, 2012.
• Abdolhosseini Qomi, M. J.; Krakowiak, K. J.; Bauchy, M.; Stewart, K. L.; Shahsavari, R.; Jagannathan, D.; Brommer, D. B.; Baronnet, A.; Buehler, M. J.; Yip, S.; Ulm, F.-J.; Van Vliet, K. J.; Pellenq, R. J.-M. “Combinatorial molecular optimization of cement hydrates.” Nature Communications 5, 4960 (2014). 
• Ling, S.; Qin, Z.; Huang, W.; Cao, S.; Kaplan, D. L.; Buehler, M. J. "Design and function of biomimetic multilayer water purification membranes." Science Advances 3(4), e1601939 (2017).
• Yu, C.-H.; Qin, Z.; Martin-Martinez, F. J.; Buehler, M. J. "A self-consistent sonification method to translate amino acid sequences into musical compositions and application in protein design using artificial intelligence." ACS Nano 13(7), 7471-7482 (2019).

Patents

• M. J. Buehler, D.L. Kaplan, S. Ling, Z. Qin, Biomimetic Multilayer Compositions, US 11,247,181 (granted 2022).

• M. J. Buehler, K. Jin, D. L. Kaplan, S. Ling, Silk Nanofibrils and Uses Thereof, US 11,643,444 (granted 2023).

• L. Wu, S. Huo, T. Ma, P.-Y. Chen, Z. Qin, E.J. Lim, F.J. Martin-Martinez, H. Sun, B. Marelli, M.J. Buehler, Method and System for Designing and Folding Structural Proteins from the Primary Amino Acid Sequence, US 12,562,236 (granted 2026).

Note: Several additional patents pending (not listed here)