About

Timothy Weihs is a professor of materials science and engineering at Johns Hopkins University, with a focus on the fabrication, characterization, and application of reactive, structural, and biodegradable materials. His research aims to understand how novel processing techniques and chemistries can produce unique microstructures and properties for scientific studies and specific applications. Weihs has developed sputter-deposited reactive multilayer foils as model materials for studying phase transformations and as local heat sources for soldering and brazing components. He co-founded Reactive NanoTechnologies to commercialize these foils and served as its CEO until 2009. His work extends into energetic materials, including reactive metal powders and structural energetic materials for bio and chemical agent defeat. Weihs directs the Materials Science in Extreme Environments University Research Alliance (MSEE), which sponsors related research. His group fabricates powders through ball-milling, ultrasonic atomization, and sputter deposition to enhance ignition and combustion properties. In structural materials, he has refined the microstructure of FeCo alloys for jet engine applications and Mg alloys for armor applications. Currently, his research involves working with Professor Falk to explore dislocations and vacancies in Mg and Al alloys, and within the Center on Artificial Intelligence for Materials in Extreme Environments (CAIMEE) to develop novel metallic alloys. In the area of biomaterials, Weihs and his students collaborate with partners to develop magnesium alloys for biodegradable medical implants, such as screws, plates, and porous scaffolds for bone repair. Their research seeks to understand the links between chemistry, microstructure, mechanical properties, and corrosion rates to ensure safe degradation and sufficient support during healing. They also utilize 3D weaving and additive manufacturing to create architected materials with improved permeability and stiffness. Weihs is a member of several professional societies, including the Materials Research Society and The Minerals, Metals and Materials Society, and is a fellow of the American Society for Metals. His awards include an NSF Career Award, a 3M Young Faculty Fellowship, an R&D 100 Award, an Innovator of the Year Award, induction into the National Academy of Inventors, and a Fulbright Fellowship.

Research topics

• Materials science
• Nanotechnology
• Metallurgy

Selected publications

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

  Open MIND · 2026-03-06

  preprintSenior author

  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

• High-throughput dynamic experiments: The statistics of spall failure at ultra-high strain rates

  Journal of the Mechanics and Physics of Solids · 2026-01-20 · 2 citations

  article
  PublisherDOI

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

  ArXiv.org · 2026-03-06

  articleOpen accessSenior author

  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

• Machine learning-assisted analysis of ignition and combustion properties in as-milled and annealed Al-Zr composite powders

  Combustion and Flame · 2025-11-25 · 2 citations

  articleSenior authorCorresponding
  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

• Swaged Aluminum Powder Compacts: Linking Processing Variables to Quasistatic and Dynamic Properties with Machine Learning

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Role of microstructure, corrosion, and pit geometry in governing strength and ductility loss in biodegradable magnesium alloy wires

  Journal of Alloys and Compounds · 2025-10-30 · 5 citations

  articleSenior authorCorresponding
  PublisherDOI

• Author Correction: A call to elevate the role of processing in AI-driven materials design

  Nature Reviews Materials · 2025-10-31

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Enhancing the Hardness–Corrosion Balance in Lean Mg–Ca Alloys via Post-Extrusion Thermomechanical Processing

  Research Square · 2025-09-13

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Impact of Extrusion, ECAP, and Annealing on the Microstructure, Mechanical Properties, and Biocorrosion Behavior of the ZX10 Magnesium Alloy

  Research Square · 2025-01-13

  preprintOpen accessSenior author
  PublisherOA PDFDOI

Recent grants

• Nucleation in Solid Metallic Solutions with Steep Composition Gradients

  NSF · $405k · 2013–2017

Frequent coauthors

• Suhas Eswarappa Prameela

  American Institute of Aeronautics and Astronautics

  61 shared

• Michael L. Falk

  46 shared

• Michael D. Grapes

  Lawrence Livermore National Laboratory

  46 shared

• David A. LaVan

  42 shared

• Omar Knio

  King Abdullah University of Science and Technology

  36 shared

• K. Woll

  Karlsruhe Institute of Technology

  32 shared

• Thomas LaGrange

  École Polytechnique Fédérale de Lausanne

  30 shared

• Laszlo J. Kecskes

  Johns Hopkins University

  29 shared

Awards & honors

• NSF Career Award
• 3M Young Faculty Fellowship
• R&D 100 Award
• Innovator of the Year Award
• TMS Application to Practice Award