The current computational project of the group has the title “Computational design of self-assembling supramolecular motors and quasicrystals.” The project is financed by CNCS-UEFISCDI (project number PN-III-P1-1.1-TE-2019-1018), and runs from September 2020 to August 2022. This is my third (and last) “Young Research Teams” project.

The field of nanotechnology transformed virtually every aspect of our lives during the last few decades. As researchers gradually understood the structural basis of complex biological processes, the need arose to rationally design artificial structures on the nanoscale that can mimic some of such processes, or interfere with them at the molecular/supramolecular level.

The present proposal aims to create new models of colloidal building blocks that can self-assemble into functional structures at this length scale. We will modify a coarse-grained model of charged rigid particles, having only ionic and excluded volume repulsive interactions, originally developed in my group for self-assembling tetravalent Goldberg hollow shells. By exploiting the observation that large-scale cooperative rearrangements between competing structures in model hollow shells have in fact very simple energetic profiles, novel colloidal motor designs will be developed, which will be able to transform external energy input into directional rotational motion. The second model to be created is for colloidal building blocks that form two-dimensional quasicrystals. Such structures are predicted to have very interesting optical properties, and can be exploited for many photonic applications. It is extremely difficult to experimentally create motors or quasicrystals from colloidal building blocks. The computational models developed in this project can guide experiments by specifying the minimal conditions for building blocks in order to be able to spontaneously form functional structures on the colloidal length scale.

Team members:

  • Istvan HORVATH, PhD student
  • Menyhart-Botond SAROSI, postdoctoral researcher
  • Szilard FEJER, principal investigator
Summary of research activities during the first stage of the project
  • setting up the simulation framework with HOOMD-Blue and Docker containers
  • exploration of parameter space for selected toy models in order to better understand their behaviour

For the technically savy readers, we have set up three Docker environments in this stage:

1. The molecular dynamics environment:
This mainly consists of the HOOMD-Blue library and some custom scripts which will be used to automatise the workflow of guessing and perfecting the simulation parameters, also a basic database which will record all the data, written in Pandas. This container can be accessed trough the built in Jupyter notebook, which is an interactive Python scripting environment.

2. The structure optimization environment:
This Docker image is built on the Python 2.7 image, with Pele and some other Python packages installed, like NumPy and SciPy for performing the required calculations and Pandas to manage data. The main role of this container is to optimise simple molecular structures, to produce input for the machine learning environment and to study the energy landscapes of molecules.

3. The machine learning environment:
This container is based on the official Tensorflow-Jupyter image, with some extra packages to support the creation of 3D representations of molecular structures. The purpose of this container is to create a neural network, which takes as input a randomly generated molecular structure and predicts a starting structure for energy minimisation, with the aim of guessing a structure that is close to a local potential energy minimum.

A draft progress report for the first stage can be downloaded from here.

Summary of research activities during the second stage of the project
  • exploring single transition state rearrangements corresponding to cooperative rotatory motion
  • finding the basic design of a self-assembling colloidal motor
  • adapting the model for explicit solvent
  • exploring different energy transfer methods to colloidal motors

The following video shows a long single transition state rearrangement between two lowest-energy structures of a closed shell:

The following animation shows sustained rotational motion for the rotor blade (a minimal model for a colloidal motor:

The following animation shows assembly of a sheed with local quasicrystalline order:

A draft progress report for the second stage can be downloaded from here.