Symposium SF07-Complexity Engineering of Materials Combining Order, Disorder and Hierarchical Organization

Learning from biology, we understand complexity as the performance-oriented structural organization of matter combining order and disorder. Complexity in various human-made and biological materials can be spontaneous but its emergence is hard to predict because it (1) originates from extensive multibody interactions; (2) incorporate diverse non-ideal components and (3) spans multiple lengths scales. The practical needs for complexity are vital and timely. They stem from the pursuit of specific combinations of properties exemplified by combinations of strength vs. lightweight, conductivity vs. transparency, and recyclability vs. environmental robustness. Incorporating complexity into material design makes it also possible to combine these and other properties while mimicking nature's energy efficiency.

The subtopics of our symposium will cover gel networks, high-performance composites, complex particles, high entropy materials, microscopy tools, additive manufacturing of complex materials using 3D printing, and self-organization phenomena. Mathematical methods applicable to complex materials exemplified by graph theory, network science, fractal mathematics, percolation theory and machine learning are also included.

Topics will include:

  • Complex materials with hierarchical organization and controlled order and disorder
  • Nanofiber composites with percolating networks
  • Metamaterials with disorder
  • Self-assembly and other pathways to complexity
  • Chiral nanostructures and multifunctional materials thereof
  • Bioinspired materials
  • Composites, ceramics and metals with high and medium entropy
  • Microscopy techniques in studying 3D nanostructures
  • Rigidity percolation, charge transport, and heat transfer in complex matter
  • Graph theoretical design parameters for composites and gels
  • Fractal and multifractal characterization of materials and particles assemblies
  • Percolation theory for materials and particles
  • Machine learning approaches for materials with functional stochasticity.
  • A tutorial complementing this symposium is tentatively planned.

Invited Speakers (tentative):

  • Archana Bhaw-Luximon (University of Mauritius, Mauritius)
  • Nadja Bigall (Institut für Physikalische Chemie und Elektrochemie, Germany)
  • Stephanie Brock (Wayne State University, USA)
  • Qian Chen (University of Illinois at Urbana-Champaign, USA)
  • Emanuela Del Gado (Georgetown University, USA)
  • Lawrence Drummy (Air Force Research Laboratory, USA)
  • Alexander Eychmueller (Technische Universität Dresden, Germany)
  • Sharon Glotzer (University of Michigan, USA)
  • Abigail Juhl (Air Force Research Laboratory, USA)
  • Dan Liu (Deakin University, Australia)
  • Xiaoming Mao (University of Michigan, USA)
  • Bridget Mutuma (University of Nairobi, Kenya)
  • Timothy Sirk (U.S. Army Research Laboratory, USA)
  • Martin Thuo (North Carolina State University, USA)

Symposium Organizers

Nicholas Kotov
University of Michigan
Materials Science
USA
No Phone for Symposium Organizer Provided , [email protected]

Paul Bogdan
University of Southern California
Electrical Engineering
USA
No Phone for Symposium Organizer Provided , [email protected]

Sanuel Chigome
Botswana Institute for Research and Technology
Materials Science
Botswana
No Phone for Symposium Organizer Provided , [email protected]

Molly Stevens
Imperial College London
Materials and Bioengineering
United Kingdom

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