Markus J. Buehler

About

Markus J. Buehler is the Jerry McAfee (1940) Professor in Engineering at the Massachusetts Institute of Technology. His research focuses on materials science and mechanics of natural and biological protein materials, exploring how protein materials define the human body and how they fail catastrophically, including fracture, deformation, and disease. His work involves large-scale atomistic modeling, protein-based materials, biopolymers, and the interaction of chemistry and mechanics, bridging chemical scales to continuum theories of materials, and developing multi-scale simulation tools. He has a background that includes a postdoctoral scholarship at the California Institute of Technology in Chemistry and Chemical Engineering, a Ph.D. in Materials Science from the Max Planck Institute for Metals Research at the University of Stuttgart, and a master's in Engineering Mechanics from Michigan Tech. Buehler is actively involved in editorial roles for several scientific journals and has received numerous awards for his contributions, including election to the National Academy of Engineering in 2023, the Washington Award in 2025, and the J.R. Rice Medal in 2022. His teaching interests encompass materials science, multi-scale modeling, biomechanics, and molecular mechanics, and he has developed and taught courses at MIT related to these fields.

Research topics

• Computer Science
• Materials science
• Artificial Intelligence
• Composite material
• Nanotechnology
• Engineering
• Biology
• Chemistry
• Chemical engineering
• Metallurgy

Selected publications

• Artificial intelligence and machine learning in design of mechanical materials

  Materials Horizons · 2020 · 629 citations

  Senior authorCorresponding

  Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional metho…

  DOI

• Hierarchically structured bioinspired nanocomposites

  Nature Materials · 2022 · 537 citations

  DOI

• Deep learning model to predict complex stress and strain fields in hierarchical composites

  Science Advances · 2021 · 365 citations

  Senior authorCorresponding

  Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory-based conditional generative adversarial neural network (cGAN), to bridge the gap between a material's microstructure-the design space-and physical performance. Our en…

  DOI

• Accumulation of collagen molecular unfolding is the mechanism of cyclic fatigue damage and failure in collagenous tissues

  Science Advances · 2020 · 115 citations

  Overuse injuries to dense collagenous tissues are common, but their etiology is poorly understood. The predominant hypothesis that micro-damage accumulation exceeds the rate of biological repair is missing a mechanistic explanation. Here, we used collagen hybridizing peptides to measure collagen molecular damage during tendon cyclic fatigue loading and computational simulations to identify potential explanations for our findings. Our results revealed that triple-helical collagen denaturation acc…

  DOI

• Exploration of Biomass-Derived Activated Carbons for Use in Vanadium Redox Flow Batteries

  ACS Sustainable Chemistry & Engineering · 2020 · 53 citations

  Senior authorCorresponding

  Increasing redox reaction rates on carbon electrodes is an important step to reducing the cost of all-vanadium redox flow batteries (VRFBs). Biomass-derived activated carbons (ACs) hold promise as they may obviate the need for post-synthetic modifications common to conventional materials. While initial efforts have shown that these materials can enhance VRFB performance, the wide selection of potentially inexpensive feedstocks and synthesis routes lead to a collection of electrocatalytic materia…

  DOI

Recent grants

• Models to Predict Protein Biomaterial Performance

  NIH · $4.6M · 2012–2022

• Models to Predict Protein Biomaterial Performance

  NIH · $601k · 2012–2017

• CAREER: Mechanics of Chemically Complex, Hierarchical Nanostructured Protein-Based Materials Under Extreme Conditions

  NSF · $400k · 2007–2013

Frequent coauthors

• Soichiro Tsuda

  252 shared

• Graham Bratzel

  Massachusetts Institute of Technology

  143 shared

• Zhao Qin

  138 shared

• Murat Okandan

  134 shared

• Darren M. Bagnall

  Macquarie University

  133 shared

• Neville C. Luhmann

  133 shared

• Gabriela Juárez-Martı́nez

  133 shared

• Melissa A. Pasquinelli

  North Carolina State University

  133 shared

Labs

• Markus J. Buehler LabPI

Education

• Ph.D., Civil Engineering

  Massachusetts Institute of Technology

  1996

• M.S., Civil Engineering

  Massachusetts Institute of Technology

  1993

• B.S., Civil Engineering

  University of California, Berkeley

  1991

Awards & honors

• Washington Award, 2025
• Elected Member, National Academy of Engineering, 2023
• J.R. Rice Medal, 2022
• TMS Hardy Award, 2013
• JOM Best Paper Award, 2013

Similar researchers at Massachusetts Institute of Technology

• Eric J. Almh-125
• Dara Entekhabih-94
• Jesse Krollh-90