Prof. Mark Richard Wilson, (Head of Department)
(email at email@example.com)
We use classical molecular dynamics and Monte Carlo simulation methods to study problems in soft matter chemistry. We concentrate on materials that are ordered on the nanoscale, including liquid crystals, colloids, polymers, amphiphilic molecules and proteins. A key part of our work involves multiscale simulations of soft matter systems, including developing and testing new coarse-grained models.
Read about our latest simulations at my research group web pages.
We are developing new coarse-grained models for soft matter systems.1 Coarse-grained models allow molecular simulation to be carried out over time scales that are not accessible to conventional atomistic simulations. Long time scales allow us to observe self-assembly in solution, study polymers, observe segregation of molecules to an interface, or see formation of complex molecular structures mediated by specific molecular interactions.
Models to understand dynamic allostery in proteins
Allostery is a process by which a molecule binding to one site of a protein alters the activity of the protein at another site. We have developed new models to help understand how protein dynamics can influence allostery. In cases where the protein undergoes no conformational change on binding a small molecule, we demonstrate that that allostery arises as a natural consequence of changes in global low-frequency protein fluctuations on ligand binding.2
Simulation of chromonic liquid crystals
We have recently carried out the first detailed study of the structure and dynamics of a chromonic liquid crystal (edicol - the food dye sunset yellow).3 The simulations show spontaneous self-assembly in water at atomistic detail, and can be used to determine the free energy of association in solution. Sunset yellow is seen to form single molecule stacks with a preference for head-to-tail ordering, which at higher concentrations form liquid crystal phases in water (as shown in the picture above). Our work shows that simulation can be used to understand, predict and control molecular self-assembly with a view to the future use of chromonic materials for applications in self-assembled molecular electronics.
Dynamics in soft solids
We have been using atomistic simulation to study dynamics in molecular solids.4 Our recent work with octafluoronaphthalene and urea inclusion compounds has indicated that its is now possible to look at dynamical processes over tens of ns, and study correlated motion in solids. This provides the possiblity of using simulation to complement and help interpret experimental solid state NMR data, and raises intriguing questions as to whether we can use simulation to help engineer tractable molecular machines for future applications.
Simulation of liquid crystal molecules and calculation of material properties
Molecular simulations provide a way of understanding the structure and dynamics5 of complex materials such as liquid crystals. We have recently used6 atomistic simulation to study the structure of the first low molecular weight biaxial nematic liquid crystal, ODBP-Ph-C7.
We have also been developing techniques to simulate liquid crystal molecules at the atomistic level and predict material properties in liquid crystal phases from simulation. For example, the rotational viscosity determines how quickly a nematic liquid crystal can reorientate in a liquid crystal display; and can be determined from our simulations by monitoring the liquid crystal director in the course of a molecular dynamics simulation.7 To carry out this work, we need accurate representations of intramolecular interactions within a molecule and intermolecular interactions between molecules. This is made possible by a new force field for use in materials chemistry, which we have been developing.8
The picture shows a sequence of time frames showing the slow growth of a biaxial phase from a uniaxial starting point. As the phase grows it develops ferroelectric domains, which are shown colour-coded. Molecules which are blue and red have transverse dipoles pointing in opposite directions.
A snapshot from a molecular dynamics simulation showing the liquid crystal molecule PCH5 in a nematic phase.
Simulation studies of amphiphilic molecules in water
We are interested in the properties of amphiphilic molecules and have used simulation to understand the behaviour of amphiphilic polymers at a water/air interface. The simulations can be used to interpret the results of complementary neutron diffraction studies by calculating neutron reflectivity curves to match with experiment.9
Similar modelling techniques are being used to study lipid - amino acid interactions. Our recent atomistic work in this area seeks to understand how these interactions (specifically the balance of hydrogen bonding and cation-Pi interactions) can be perturbed by small chemical modifications of the amino acid structure. Such studies seek to aid our understanding of binding between proteins and cell membranes.
Snapshots from a simulation study of an amphiphilic polymer at a water-air interface at low surface concentration. The hydrophobic polynorbornene backbone is shown in blue and the hydrophilic poly(ethylene oxide) (PEO) grafts are shown in red. The dots represent water molecules. At higher surface concentrations the PEO chains are forced down into the aqueous subphase to form a polymer brush.
Coarse-grained models for the simulation of polymers, dendrimers, liquid crystals and lipids
We have developed new coarse-grained models to study the bulk structure of liquid crystalline polymers and dendrimers10 and are working on “molecular engineering” tools to design molecules to have the desired organisation at the nanoscale. This research is being extended to lipid bilayers and vesicles. An important part of our work has involved developing efficient simulation methods that make use of parallel computers, so we are able to simulate extremely large systems composed of many molecules and take advantage of today’s supercomputers.11
The figure shows microphase separation in a liquid crystalline side chain polymer to give polymer-rich and liquid crystal-rich regions. Blue: polymer backbone (methylsiloxane), white: liquid crystal group, red: flexible spacer groups (alkyl chain).
More on Soft Matter Simulations
- Prasitnok K., and Wilson M. R. A coarse-grained model for polyethylene glycol in bulk water and at a water/air interface. Phys. Chem. Chem. Phys., 2013, 15, 17093-1710.
- Rodgers T. L., Townsend P. D., Burnell D., Jones M. L., Richards S. A., McLeish T. C. B., Pohl E., Wilson M. R., Cann M. J. Modulation of Global Low-Frequency Motions Underlies Allosteric Regulation: Demonstration in CRP/FNR Family Transcription Factors. PLoS Biol, 2013, 11(9): e1001651.
- Chami F. and Wilson M. R. Molecular Order in a Chromonic Liquid Crystal: A Molecular Simulation Study of the Anionic Azo Dye Sunset Yellow. J. Am. Chem. Soc., 2010, 132, 7794.
- Ilott A. J., Palucha S., Batsanov A. S., Wilson M. R. and Hodgkinson P. Elucidation of Structure and Dynamics in Solid Octafluoronaphthalene from Combined NMR, Diffraction, And Molecular Dynamics Studies. J. Am. Chem. Soc., 2010, 132, 5179.
- Oganesyan, V. S., Kuprusevicius, E., Gopee, H., Cammidge, A. N., Wilson, M. R. Simulation of EPR spectra directly from molecular dynamics trajectories of a liquid crystal with doped paramagnetic spin probe. Phys. Rev. Lett., 2009, 102, 013005(1)-013005(4).
- Pelaez J. and Wilson M. R. Atomistic Simulations of a Thermotropic Biaxial Liquid Crystal. Phys. Rev. Lett., 2006, 97., 267801.
- Cheung D. L., Clark S. J, Wilson M. R. Calculation of the rotational viscosity of a nematic liquid crystal. Chem. Phys. Lett., 2002, 356, 140.
- Cheung D. L., Clark S. J., Wilson M. R. Parametrization and validation of a force field for liquid-crystal forming molecules. Phys. Rev. E, 2002, 65,051709.
- Anderson P. M. and Wilson M. R. Molecular Dynamics Simulations of an Amphiphilic Graft Copolymer at a Water/Air Interface., 2004, J. Chem. Phys, 121, 8503.
- Wilson M. R., Ilnytskyi J. M., Stimson L. M., Computer simulations of a liquid crystalline dendrimer in liquid crystalline solvents. J. Chem. Phys., 2003, 119, 3509.
- Ilnytskyi J, Wilson M. R.. A domain decomposition molecular dynamics program for the simulation of flexible molecules with an arbitrary topology of Lennard-Jones and/or Gay-Berne sites. Comput. Phys. Comm., 2001, 134, 23.
- Earl D. J., M., Wilson M. R. Predictions of molecular chirality and helical twisting powers: A theoretical study. J. Chem. Phys., 2003, 119, 10280.
Department of Chemistry
- Theoretical and Computational Chemistry
- Soft Matter
- Liquid Crystals and Polymers
- Walker, Martin & Wilson, Mark R. (2016). Formation of complex self-assembled aggregates in non-ionic chromonics: dimer and trimer columns, layer structures and spontaneous chirality. Soft Matter 12(41): 8588-8594.
- Walker, Martin & Wilson, Mark R. (2016). Simulation insights into the role of antiparallel molecular association in the formation of smectic A phases. Soft Matter
- Boyd, Nicola Jane & Wilson, Mark Richard (2015). Optimization of the GAFF Force Field to Describe Liquid Crystal Molecules: The Path to a Dramatic Improvement in Transition Temperature Predictions. Physical Chemistry Chemical Physics 17(38): 24851-24865.
- Akinshina, A., Walker, M., Wilson, M. R., Tiddy, G. J. T., Masters, A. J. & Carbone, P. (2015). Thermodynamics of the self-assembly of non-ionic chromonic molecules using atomistic simulations. The case of TP6EO2M in aqueous solution. Soft Matter 11(4): 680-691.
- Walker, M., Masters, A. J. & Wilson, M. R. (2014). Self-assembly and mesophase formation in a non-ionic chromonic liquid crystal system: insights from dissipative particle dynamics simulations. Physical Chemistry Chemical Physics 16(42): 23074-23081.
- Prasitnok, Khongvit & Wilson, Mark R. (2013). A coarse-grained model for polyethylene glycol in bulk water and at a water/air interface. Physical Chemistry Chemical Physics 15(40): 17093-17104.
- McLeish, Thomas C. B., Rodgers, T. L. & Wilson, Mark R. (2013). Allostery without conformation change: modelling protein dynamics at multiple scales. Physical Biology 10(5): 056004.