AMRL connects materials design to materials manufacturing and performance. Using computational and experimental methods, we develop novel methods for accelerating the transition of materials from design inception to deployment. AMRL tools and capabilities help realize the goals of computational materials design and connect synthesis to scalable materials processing for a circular economy. Some examples of materials under consideration are provided below:

Data-enabled Discovery of Materials

Develop an ability to steer searches compounds, alloys, and metastable states with unique properties using genetic algorithms for generations of crystal structure guesses with progressively better structural motif or stability.

  1. A search of alloys and compounds in the phase diagram
  2. Materials for a circular economy
  3. Design of corrosion-resistant microstructure
  4. Nanostructured coatings
  5. Design of molecular and hybrid crystals
  6. Additive-manufacturing
  7. 3D printing of electronics and sensors
  8. Bio-based and bio-sourced materials such as bioplastics
materials science and engineering's connection to manufacturing and IoT

As articulated in a recent DOE report  “The ability to predict and control mesoscale phenomena and architectures is essential if atomic and molecular knowledge is to blossom into the next generation of technology opportunities, societal benefits, and scientific advances”. The challenge of modeling phenomena in the mesoscale is scaling the system size and the physics to the boundaries of nano-and-microscale where continuum behaviors emerge. Due to recent advances in precision in 3D printing, mesoscale assembly of materials through proper control of transport, reactions, and phase segregation processes are possible. However, there remain large gaps in understanding mesoscale. The outcome of the research at AMRL is an ability to predict Structure-Property-Performance-and-Processing (SPPP) correlations in complex mesoscale architectures under dynamic thermo-chemo-mechanical conditions and bridge fundamental science constructs to the engineering scale.