Opportunities in theoretical and computational polymeric materials and soft matter.
@article{Liu2015OpportunitiesIT, title={Opportunities in theoretical and computational polymeric materials and soft matter.}, author={Andrea J. Liu and Gary S. Grest and M. Cristina Marchetti and Gregory M. Grason and Mark O. Robbins and Glenn H. Fredrickson and Michael Rubinstein and Monica Olvera de la Cruz}, journal={Soft matter}, year={2015}, volume={11 12}, pages={ 2326-32 } }
Soft materials are abundant in nature and ubiquitous in living systems. Elucidating their multi-faceted properties and underlying mechanisms is not only theoretically challenging and important in its own right, but also serves as the foundation for new materials and applications that will have wide-ranging impact on technology and the national economy. Recent initiatives in computation and data-driven materials discovery, such as the Materials Genome Initiative and the National Science…
27 Citations
Computational framework for polymer synthesis to study dielectric properties using polarizable reactive molecular dynamics.
- Physics, Materials Science
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The increased energy and power density required in modern electronics poses a challenge for designing new dielectric polymer materials with high energy density while maintaining low loss at high…
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- Materials SciencePhysical chemistry chemical physics : PCCP
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A conceptually new design principle is reported for creating supracolloidal fullerene-like cages through the self-assembly of soft patchy particles interacting via directional nonbonded interactions by mimicking non-planar sp2 hybridized carbon atoms in C60.
Polymer Nanoparticle Assemblies: A Versatile Route to Functional Mesostructures
- Materials Science
- 2015
Fabricating macromolecular mesoscale assemblies containing disparate components with targeted molecular order for each of the components on the nanoscale and targeted assembly of the components in…
Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach
- Materials Science
- 2017
A computational method for understanding and optimizing the properties of complex physical systems is presented using polymeric dispersants as an example. Concentrated suspensions are formulated with…
Copolymerization on Selective Substrates: Experimental Test and Computer Simulations.
- Materials ScienceLangmuir : the ACS journal of surfaces and colloids
- 2017
The influence of a selective substrate on the composition and sequence statistics during the free radical copolymerization of styrene and acrylic acid in bulk and in silica pores of different sizes is explored.
Copolymerization of Partly Incompatible Monomers: An Insight from Computer Simulations
- Materials Science
- 2017
We used dissipative particle dynamics simulations to study the copolymerization process in the presence of spatial heterogeneities caused by incompatibility between polymerizing monomers. We…
Shape control and compartmentalization in active colloidal cells
- EngineeringProceedings of the National Academy of Sciences
- 2015
It is demonstrated that the shape of the active colloidal cell can be controlled and compartmentalized by varying the details of the boundary and the character of the spinners and the result is a bubble–crescent configuration, which alternates between two degenerate states over time and exhibits collective migration of the fluid along the boundary.
Cell Model Approaches for Predicting the Swelling and Mechanical Properties of Polyelectrolyte Gels
- Materials ScienceMacromolecules
- 2019
We present two successive mean-field approximations for describing the mechanical properties and the swelling equilibrium of polyelectrolyte gels in contact with a salt solution. The first mean-field…
A CGenFF‐based force field for simulations of peptoids with both cis and trans peptide bonds
- Chemistry, BiologyJ. Comput. Chem.
- 2019
CHARMM general force field (CGenFF) parameters developed to accurately represent peptoid conformational behavior are presented, with an emphasis on a correct representation of both the cis and trans isomers of the peptoid backbone.
Machine learning and data science in soft materials engineering.
- Computer ScienceJournal of physics. Condensed matter : an Institute of Physics journal
- 2018
This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools.
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