About

Todd Hufnagel is a professor of materials science and engineering at Johns Hopkins University. He is an expert on structural materials, nanomaterials, X-ray scattering, 3-D microstructures, and metals. He serves as the associate director of the Materials Science in Extreme Environments University Research Alliance (MSEE URA), a group of 18 major research institutions focused on mitigating the threats posed by chemical, biological, and nuclear weapons. Dr. Hufnagel earned his BS in metallurgical engineering from Michigan Technological University in 1989, and his MS and PhD in materials science and engineering from Stanford University in 1991 and 1995, respectively. He joined Johns Hopkins University in 1996.

Research topics

• Materials science
• Composite material
• Metallurgy
• Crystallography
• Condensed matter physics

Selected publications

• AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments

  ArXiv.org · 2026-03-06

  articleOpen access1st authorCorresponding

  Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control.

  PublisherOA PDF

• AIMD-L: An automated laboratory for high-throughput characterization of structural materials for extreme environments

  Open MIND · 2026-03-06

  preprint1st authorCorresponding

  Rapid developments in artificial intelligence and machine learning as applied to materials science are creating an urgent need for experimental data, which can be provided by high-throughput and autonomous laboratories. To date most demonstrations of such laboratories have focused on functional materials, with less attention paid to structural materials. We present here the Artificial Intelligence in Materials Design Laboratory (AIMD-L), an automated, high-throughput facility for characterizing the microstructure and properties of structural metals and ceramics, with an emphasis on materials in extreme environments. AIMD-L has two custom instruments for characterization of structural materials: HELIX for shock studies of materials, and MAXIMA for X-ray diffraction and X-ray fluorescence spectroscopy. Specifically designed for high-throughput studies, HELIX and MAXIMA are each capable of collecting data at rates two to three orders of magnitude faster than conventional systems. A third experimental station, SPHINX, is a commercial nanoindenter modified for integration into the automated workflow of AIMD-L. A user (which may be human or an AI agent) directs the experiments to be carried out by means of a centralized control program. The experimental stations are linked by a conveyance that moves samples around the lab, with a robot at each station for sample transfer in/out of the instrument. The experimental stations also communicate with a common data layer that streams data autonomously from each instrument to a data portal, where their arrival triggers automated workflows for data reduction and analysis. The processed data are immediately available to the human operator or agentic AI, forming a closed loop for rapid decision-making and experimental control.

  DOI

• A deep UV Fourier ptychographic microscope for microstructural characterization

  2026-03-04

  article

  Characterization of surface topography is a crucial step in device manufacturing and material design that requires high resolution, 3D maps of large areas. Optics are limited to either optimizing for field-of-view (FOV) or resolution. Fourier ptychography (FP) surpasses this limit by combining the k-spaces of multiple, low resolution measurements at different illumination angles to produce a higher resolution image. By solving for the amplitude and phase of the object, FP also reconstructs topography. Moreover, shorter imaging wavelengths are capable of resolving finer features. We present a novel deep UV Fourier ptychographic microscope design for microstructural characterization. A large space bandwidth product (SBP) is achieved by using dual-axis galvanometer (galvo) mirrors to steer the illuminating beam to implement Fourier ptychography and translation stages to image a large area. Use of Fourier ptychography improved system resolution from 0.25 μm to 0.20 μm and accurately reconstructed the surface topography of a test indentation. Scanning a copper sample over 225 positions effectively increased the FOV 75× from 2.6 × 10⁻²mm² to 1.9mm². The DUV FP microscope’s innovative design is advantageous to material and device manufacturing by enabling high resolution topographic imaging over large areas and thus facilitating accurate, large scale characterization of sample surfaces.

  PublisherDOI

• Impact damage across length scales in concrete: Macro-, micro-, and nano-scale damage characterization

  International Journal of Impact Engineering · 2026-04-18

  article
  PublisherDOI

• Quantitative in situ studies of dynamic fracture in a lithium metasilicate glass‐ceramic by x‐ray phase contrast imaging

  Journal of the American Ceramic Society · 2025-10-16 · 1 citations

  articleSenior authorCorresponding

  Abstract Glass‐ceramics are produced through controlled crystallization of base glass, with many of their properties depending on the specific microstructures. With respect to their mechanical properties, although this dependence has been widely studied under quasi‐static loading conditions, limited studies have been carried out beyond the quasi‐static regime, especially in the context of fracture. Here, we study the fracture of lithium metasilicate glass‐ceramics having different microstructures but nominally identical mechanical properties, under dynamic three‐point‐bend loading conditions. Using time‐resolved x‐ray phase contrast imaging, we capture crack initiation and propagation in glass‐ceramics specimens and quantify the crack tip speed evolution. We find that the crack speed differs for specimens possessing different microstructures, an observation that cannot be captured by linear elastic fracture mechanics theory via a standard homogenization modeling procedure. Postmortem characterizations of fracture surfaces aided by scanning electron microscopy and white light interferometry reveal strong crack‐crystal interactions (e.g., trans‐granular fracture) and identify a correlation between a lower crack speed and an increased roughness of the fracture surface. Our work demonstrates microstructure‐modulated fracture behavior in glass‐ceramics and brings up the scale interplay between material heterogeneity and homogenization in the context of modeling fracture in heterogeneous materials.

  PublisherDOI

• Mechanisms of spall failure in niobium subjected to high-throughput laser-driven micro-flyer impact

  Acta Materialia · 2025-05-09 · 8 citations

  articleOpen access

  Designing bcc alloys for shock resistance requires a fundamental understanding of the microstructural evolution and failure mechanisms under dynamic loading. However, two significant challenges arise. Firstly, conventional plate impact experiments are time-consuming and costly, limiting the acquisition of sufficient data to investigate these mechanisms. Secondly, the mechanistic understanding of deformation and failure in pure bcc metals remains limited, hindering purposeful alloy design strategies. To address these challenges, we employ a high-throughput laser-driven micro-flyer technique to establish the relationship between the spall failure of pure bcc niobium and its evolving microstructure at tensile strain rates of ∼ 10⁶ s⁻¹. By varying flyer thickness, peak shock stress ranging from 7.3 to 15.3 GPa were achieved. Post-mortem microstructural analysis of samples recovered at incipient and advanced spall failure states reveals that failure occurs in a ductile manner through mixed intergranular and intragranular fractures. For cavities nucleating within grains, dislocation emission from void surfaces is identified as the controlling void growth mechanism up to a void radius of ∼260 nm, after which cracks emanate from void surfaces extending outward along {101} planes. Local misorientation variations are observed around the cracks, with dislocation cells observed away from crack edges and high-angle grain boundaries at crack edges, revealing continuous dynamic recrystallization in regions of highly accumulated plastic strain. Our spall strength and microstructural evolution results are discussed in the context of analytical models for dynamic cavitation-driven failure.

  PublisherDOI

• Bayesian inference and GPSR-based void nucleation probability model for polycrystalline Al alloys for spall prediction

  International Journal of Plasticity · 2025-07-21 · 3 citations

  article
  PublisherDOI

• Automated multi-object tracking: Applications to metal combustion under XPCI

  Computational Materials Science · 2025-11-01

  articleOpen access

  This study introduces the XPCI Multi-object Tracker (XMOT), a tool designed for the automated analysis of X-ray Phase Contrast Imaging (XPCI) videos that capture the combustion of metal composite powders. While tailored for XPCI data, the design and methods behind XMOT are general and can be applied to many kinds of scientific imaging of dynamic processes. XMOT automates the detection of particles and the construction of their trajectories, greatly improving the efficiency of data analysis. This methodology allows for the quantification of dynamic and static particle properties and has been used to demonstrate that micro-explosions occur in both spherical and non-spherical particles. Such data is crucial for evaluating combustion mechanisms and performance. Validation demonstrates that XMOT achieves about 90 % accuracy and 74 % detection coverage in the particle detection step, and shape classification accuracy of about 70 % for spherical particles and about 85 % for non-spherical particles. By automating complex, labor-intensive processes, XMOT facilitates deeper insights into the relationships between material properties and combustion performance, paving the way for advanced material design and optimization. • Automated characterization of metal particle combustion using XPCI. • Multi-object tracking with GMMs, Kalman filters and Hungarian algorithms. • Identification of secondary events (eg. microexplosions) and particle features (eg. sphericity) during combustion. • Generalizable framework for dynamic imaging.

  PublisherDOI

• Shack–Hartmann wavefront sensing: A new approach to time-resolved measurement of the stress intensity factor during dynamic fracture

  Mechanics of Materials · 2024-04-12 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

• Corrigendum to “Efficient searching of processing parameter space to enable inverse microstructural design of materials” [Acta Materialia 264 (2024) 119562]

  Acta Materialia · 2024-02-26

  erratumSenior authorCorresponding
  PublisherDOI

Recent grants

• Nanometer-Scale Structure and Properties of Amorphous Alloys

  NSF · $410k · 2003–2008

• Materials World Network: Nanoscale Studies of Fundamental Mechanisms of Deformation in Amorphous Materials

  NSF · $600k · 2011–2015

• The Structural Basis for Fracture Toughness and Elasticity of Metallic Glasses

  NSF · $400k · 2007–2012

• DMREF: Data-Driven Integration of Experiments and Multi-Scale Modeling for Accelerated Development of Aluminum Alloys

  NSF · $2.1M · 2020–2024

• Measurement and mechanisms of elastic deformation in amorphous solids

  NSF · $360k · 2014–2017

Frequent coauthors

• K.T. Ramesh

  Johns Hopkins University

  29 shared

• Timothy P. Weihs

  Johns Hopkins University

  14 shared

• B Clemens

  Stanford University

  13 shared

• Ryan Ott

  13 shared

• Xiaofeng Gu

  Xinjiang Normal University

  13 shared

• Sol M. Grüner

  Cornell University

  13 shared

• Wendelin J. Wright

  Bucknell University

  12 shared

• Andrew F. T. Leong

  10 shared

Education

• Ph.D. Materials Science and Engineering, Materials Science and Engineering

  Stanford University

  1995

• M.S. Materials Science and Engineering, Materials Science and Engineering

  Stanford University

  1991

• B.S. Metallurgical Engineering, Metallurgical Engineering

  Michigan Technological University

  1989

Awards & honors

• Whiting School of Engineering Teaching, Advising, and Mentor…