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AMRL is working on advancing multiscale science and computational tools for the design, synthesis, and scale-up of advanced materials. We focus our physics-based modeling on the design and scale-up of polymers, catalysts, battery materials, coatings, composites, molecular crystals, 2D materials for conductive ink, and alloys for energy and environmental applications.

Research Focus

  • Mesoscale and Multiphysics Modeling: Connection of atomic scale averages to the mesoscale using coarse-grained, discrete element, fluid dynamics, and finite element models; multiphysics models of materials processing and manufacturing
  • First-Principles and Molecular Dynamics Simulations:  Density functional theory (DFT) for solids, alloys, and compounds; classical, reactive, and coarse-grain molecular dynamics of materials.
  • Data and Machine Learning: High-throughput DFT databases, automation of simulations and learning from molecular dynamics, machine learning for the acceleration of first-principles thermodynamic calculations, machine learning for materials property predictions, reinforcement learning for steering of experiments and manufacturing processes.
  • High-performance Computing: Integration of edge-to-exascale computing and machine learning acceleration hardware in advanced materials design, scale-up, and manufacturing.

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.