de Pablo Group

Our group investigates the physics and thermodynamics of complex materials using statistical mechanics, molecular simulations, and machine learning.  Using the results, we design new systems for technological applications.

 

Principal Investigator

Juan de Pablo

depablo@uchicago.edu

Thermodynamics and Structure of Poly[n]catenane Melts

Rauscher,P. M., Schweizer, K. S. , Rowan, S. J. , de Pablo, J. J. Macromolecules ASAP

Structuring Stress for Active Materials Control

Zhang, Rui, et al. "Structuring Stress for Active Materials Control." arXiv preprint arXiv:1912.01630 (2019).

Ultrathin initiated chemical vapor deposition polymer interfacial energy control for directed self-assembly hole-shrink applications

Dolejsi, Moshe, et al. "Ultrathin initiated chemical vapor deposition polymer interfacial energy control for directed self-assembly hole-shrink applications." Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena 37.6 (2019): 061804.

Sculpted grain boundaries in soft crystals

Li, Xiao, et al. "Sculpted grain boundaries in soft crystals." Science Advances 5.11 (2019): eaax9112.

Reconfigurable Multicompartment Emulsion Drops Formed by Nematic Liquid Crystals and Immiscible Perfluorocarbon Oils

Wang, Xin, et al. "Reconfigurable Multicompartment Emulsion Drops Formed by Nematic Liquid Crystals and Immiscible Perfluorocarbon Oils." Langmuir 35.49 (2019): 16312-16323.

A diversified machine learning strategy for predicting and understanding molecular melting points

Jackson, Nicholas, et al. "A diversified machine learning strategy for predicting and understanding molecular melting points." (2019).

Controlling Complex Coacervation via Random Polyelectrolyte Sequences

Rumyantsev, Artem M., et al. "Controlling Complex Coacervation via Random Polyelectrolyte Sequences." ACS Macro Letters 8.10 (2019): 1296-1302.

Emergence of Radial Tree of Bend Stripes in Active Nematics

Sokolov, Andrey; Mozaffari, Ali; Zhang, Rui; de Pablo, Juan; and Snezhko, Alexey. Emergence of Radial Tree of Bend Stripes in Active Nematics. PHYSICAL REVIEW X. 2019. Vol. 9, Pg. 031014.

Liquid Crystalline and Isotropic Coacervates of Semiflexible Polyanions and Flexible Polycations

Artem M. Rumyantsev and Juan J. de Pablo. Liquid Crystalline and Isotropic Coacervates of Semiflexible Polyanions and Flexible Polycations. Macromolecules. 2019. Vol. 52, Pg. 5140-5156.

Role of Defects in Ion Transport in Block Copolymer Electrolytes

Kambe, Yu, et al. "Role of Defects in Ion Transport in Block Copolymer Electrolytes." Nano Letters (2019).

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Liquid crystals (LCs) are a phase of matter that flows like a liquid, but the orientations of the molecules are highly ordered over a very long range. This presence of long-range orientation results in interesting behavior of systems that employ LCs. In our group, we model LCs on multiple scales in an effort to engineer new applications for the laboratory and industry. At the atomistic level, we investigate the behavior of LCs near surfaces to determine the types and strength of anchoring present at different surfaces. At a mesoscale, we study systems mixtures and determine the accessibility different phases of LCs. On the largest scales, we investigate the behavior of particles, from the nanometer to micron scale, and observe their behavior in an LC solvent; the presence of defects in the LC has a marked effect on particle behavior, so by controlling the defect with fields (flow, electric, magnetic,etc.), we can dictate particle behavior in a well controlled manner.  We also study how introducing active biological agents, such as bacteria or myosin motors, influences the dynamics of LC systems.

Our research group uses coarse grain models to study the biophysics of DNA and chromatin. Recent efforts have included:

  1. Developing hierarchical coarse-grained models to study DNA and chromatin at multiple length scales.
  2. Relating nucleosome interactions to chromatin fiber structure
  3. Exploring collective motions in chromatin using nonlinear manifold learning
  4. Examining the effect of DNA-protein interactions on the structure and dynamics of chromatin

With the help of modern polymer chemistry, macromolecules can now be synthesized with exquisite control over architecture, composition, and monomer sequence. These parameters work cooperatively to control the dynamics, self-assembly, and morphology of the resulting materials and determine how they may be applied in new technologies.  To understand and predict these properties, we develop and utilize multi-scale Monte Carlo and molecular dynamics simulations as well as novel theoretical treatments.  Our current ares of research include:

  • Controlling complex coacervation of polyelectrolytes with random charge sequences
  • Structure and rheology of "miktoarm" polymers and their blends
  • Using multi-block co-polymers to design complex nanostructures in solution
  • Dynamics of interlocking polymers

 

Molecular simulations are typically limited by the time scale of sampling. With reasonable amount of computational resources one can only simulate on the order of hundreds of nanoseconds. On the other hand in real complex systems most of the phenomena of interest occur at orders-of-magnitude longer time scales. To solve such problems, we develop new advanced sampling algorithms that can accelerate discovery in these systems in both Monte Carlo (MC) and Molecular Dynamics (MD) simulations, which have been incorporated into the Software Suite for Advanced General Ensemble Simulations (SSAGES) code.

Many soft matter systems exhibit a wide range of length scales, making them challenging to simulate and study.  For example, colloidal suspensions are naturally represented as discrete particles embedded in a continuum since the colloids are much larger than the individual molecules of the solvent.  To couple the dynamics of particles and continua, we have developed the Continuum-Particle Simulation Software (COPSS) code, which uses innovative numerical methods to efficiently simulate mesoscopic soft matter systems.

The SSAGES and COPSS codes are available at: http://miccomcodes.org/

 

Machine learning provides a powerful set of methods for extracting useful information from large and complex data sets, such as those routinely generated by molecular simulations.  Our group uses these two complementary tools to gain insight into the properties of molecular systems and to make predictions for materials design.  Recent efforts include:

  • Using artificial neural networks (ANNs) for force-biasing during molecular dynamics simulations
  • Predicting electronic structure properties of conjugated polymers at coarse-grained resolution using ANNs
  • Inverse design of materials based on desired physical properties