#1. Advances in Methods for Bridging Spatiotemporal Scales in Soft Matter, Polymer and Network Materials


  • Jack F. Douglas, National Institute of Standards and Technology, USA
  • Sinan Keten, Northwestern University, USA
  • Frederick R. Phelan Jr., National Institute of Standards and Technology, USA (frederick.phelan@nist.gov)
  • Matej Praprotnik, National Institute of Chemistry and University of Ljubljana, Slovenia
  • Jay Schieber, Illinois Institute of Technology, USA
  • Wenjie Xia, North Dakota State University, USA
  • Pratyush Tiwary, University of Maryland, USA
  • Omar Valsson, Max Planck Institute for Polymer Research, Germany


The present grand challenge in the modeling of soft materials is to increasingly bridge simulations to experimentally relevant length- and time-scales. Progress towards this goal requires development of an integrated approach in which advances in modeling techniques are more wholly integrated with theory driven analysis, computational and experimental data, and emerging techniques in artificial intelligence and machine learning (AI/ML).

In advanced modeling, numerous approaches have been developed pertinent to the above paradigm. Coarse-graining methods, in which many atoms are subsumed into less chemically detailed granular descriptions, allow access to both greater length- and time-scales at much faster simulation times. Multiscale methods that concurrently couple multiple models at different resolutions, often in an adaptive fashion, enable bridging of length-scales. On the time-scale side, numerous enhanced sampling methods have been developed to overcome the rare event problem of conventional MD simulations, including methods such as metadynamics, transition path sampling, and hyperdynamics. Recently, there have also been introduced various noteworthy applications of ideas from machine learning within enhanced sampling, including methods for finding reaction coordinates on-the-fly.

The aim of this symposium is to survey recent developments and applications in advanced modeling methods in soft mater simulations. Further, we want to promote discussions on their augmentation or integration with meta-theoretical frameworks, data-driven approaches or machine learning algorithms that enable prediction or enhanced interpretation of experimental observations.

Topics of interest include:
• Advances in quantitative coarse-graining and state-point transferability
• Development of novel concurrent multiscale methods
• New developments in enhanced sampling methods
• Usage of enhanced sampling methods in concurrent multiscale simulations
• Usage of AI/ML in enhanced sampling and multiscale simulations
• AI/ML approaches to FF development and property prediction
• DFT, Ab-initio, ReaxFF approaches for molecular simulations of polymers and soft materials
• Applications in soft matter, such as, but not limited to: glass-formation, nucleation and crystal growth, molecular crystals, polymorph exploration, self-assembly, polymer mechanics and rheology, biomaterials (e.g., membranes and proteins), fluorogenic materials