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Call for Papers

Abstracts were due June 15, 2020.

Symposium F.MT04-Using Machine Learning and Multiscale Modeling to Study Soft Materials and Interfaces

This symposium is inspired by the Materials Genome Initiative and is focused on study of synthetic and natural soft-materials using theoretical and computational modeling, and machine-learning methods. The nature of nanoscopic units (e.g. biomolecules, polymers etc.) and their macroscopic structures (e.g. micelles, fibers, bilayers, vesicles, helices, emulsions, foams etc.) often determine the properties and applications of the architectures of soft-materials. Recent advances in the experimental characterization techniques have been helpful in improving our understanding of these systems to a certain extent. However, it is still very challenging to predict the final macroscopic structure, properties, stimuli-sensitivity, and emerging collective phenomena, a priori, from knowledge of the atomic constituents of nanoscopic units and processing parameters (solvent, temperature, gas environments etc.). This definitely limits our ability to further develop and improve new soft materials with predefined structure, properties, and function. To overcome these limitations, to test and validate the design, and to predict the characteristics of architectures of soft-materials with precision, advanced multiscale modeling methods and theory combined with machine learning and the state-of-art high performance computing has been employed. This symposium will discuss the design and characterization of structural and dynamical properties of soft materials using machine learning and computational modeling.

Topics will include:

  • Using modeling for processing and industrial applications of soft-materials
  • Fluids and the role of solvents
  • Simulations of self- and directed-assembly of biopolymers, biomolecules, polymers, organic molecules, and nano, magnetic and colloids materials
  • Modeling active biological and synthetic materials and their collective phenomena
  • Prediction, design and explanation of stimuli-responsive and adaptive material properties
  • Use of big data, machine learning, and optimization algorithms in modeling soft-matter
  • Processes and properties at surfaces and the interfaces
  • Simulation methods and model development (mesoscale, coarse-grain, atomistic, quantum) for soft materials
  • Modeling of soft-, biological-, bio-inspired and biomimetic materials across spatial and temporal scales

Invited Speakers:

  • Igor Aronson (Penn State University, USA)
  • Amanda Barnard (The Australian National University, Australia)
  • Meenakshi Dutt (Rutgers, The State University of New Jersey, USA)
  • Andrew Ferguson (National Renewable Energy Laboratory, USA)
  • Laura Filion (Utrecht University, Netherlands)
  • Valeriy Ginzburg (Dow Chemicals, USA)
  • Lisa Hall (The Ohio State University, USA)
  • Arthi Jayaraman (University of Delaware, USA)
  • Alfredo Alexander Katz (Massachusetts Institute of Technology, USA)
  • Sanat Kumar (Columbia University, USA)
  • Jianing Li (University of Vermont, USA)
  • Jane Lipson (Dartmouth College, USA)
  • Rampi Ramprasad (Georgia Institute of Technology, USA)
  • Marisol Ripoll (Forschungszentrum Jülich GmbH, Germany)
  • Subramanian Sankaranarayanan (Argonne National Laboratory, USA)
  • Durba Sengupta (National Chemical Laboratory, India)
  • Letizia Tavagnacco (Sapienza University, Italy)
  • Yaroslova Yingling (North Carolina State University, USA)

Symposium Organizers

Emanuela Zaccarelli
Consiglio Nazionale delle Ricerche
CNR-ISC (National Research Council - Institute for Complex Systems)
Italy

Anne-Virginie Salsac
Universite de Technologie de Compiegne
Biomechanics and Bioengineering Laboratory
France
+33 (0)3 44 23 73 38, a.salsac@utc.fr

Alexander Alexeev
Georgia Institute of Technology
George W. Woodruff School of Mechanical Engineering
USA

Sanket A. Deshmukh
Virginia Tech
Department of Chemical Engineering
USA
540-231-8785, sanketad@vt.edu