Inverse Design for Self Assembly via On-the-Fly Optimization

@article{Lindquist2016InverseDF,
  title={Inverse Design for Self Assembly via On-the-Fly Optimization},
  author={Beth A. Lindquist and Ryan B. Jadrich and Thomas M Truskett},
  journal={arXiv: Statistical Mechanics},
  year={2016}
}
Inverse methods of statistical mechanics have facilitated the discovery of pair potentials that stabilize a wide variety of targeted lattices at zero temperature. However, such methods are complicated by the need to compare, within the optimization framework, the energy of the desired lattice to all possibly relevant competing structures, which are not generally known in advance. Furthermore, ground-state stability does not guarantee that the target will readily assemble from the fluid upon… 

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References

SHOWING 1-10 OF 27 REFERENCES

Inverse optimization techniques for targeted self-assembly

This article reviews recent inverse statistical-mechanical methodologies that have devised to optimize interaction potentials in soft matter systems that correspond to stable “target” structures and envision being able to “tailor” potentials that produce varying degrees of disorder, thus extending the traditional idea of self-assembly to incorporate both amorphous and crystalline structures as well as quasicrystals.

Breadth versus depth: Interactions that stabilize particle assemblies to changes in density or temperature.

We use inverse methods of statistical mechanics to explore trade-offs associated with designing interactions to stabilize self-assembled structures against changes in density or temperature.

Optimized interactions for targeted self-assembly: application to a honeycomb lattice.

An inverse statistical-mechanical methodology to find optimized interaction potentials that lead spontaneously to a target many-particle configuration, extending the traditional idea of self-assembly to incorporate both amorphous and crystalline structures as well as quasicrystals.

Nonlinear machine learning of patchy colloid self-assembly pathways and mechanisms.

An approach to infer systematically assembly pathways and mechanisms by nonlinear data mining of molecular simulation trajectories using diffusion maps is presented and validated in applications to Brownian dynamics simulations of the assembly of anisotropic "patchy colloids" into polyhedral aggregation.

Probing the limitations of isotropic pair potentials to produce ground-state structural extremes via inverse statistical mechanics.

This work demonstrates that single-component systems with short-range radial pair potentials can counterintuitively self-assemble into crystal ground states with low symmetry and different local structural environments.

Dimensionality and design of isotropic interactions that stabilize honeycomb, square, simple cubic, and diamond lattices

We use inverse methods of statistical mechanics and computer simulations to investigate whether an isotropic interaction designed to stabilize a given two-dimensional (2D) lattice will also favor an

Optimizing topographical templates for directed self-assembly of block copolymers via inverse design simulations.

An inverse design algorithm has been developed that predicts the necessary topographical template needed to direct the self-assembly of a diblock copolymer to produce a given complex target

Assembly of nothing: equilibrium fluids with designed structured porosity.

Inverse methods of statistical mechanics are used to design an isotropic pair interaction that, in the absence of an external field, assembles particles into an inhomogeneous fluid matrix surrounding pores of prescribed size ordered in a lattice morphology.

Two-dimensional colloidal fluids exhibiting pattern formation.

A lattice-gas (generalised Ising) model is developed and the phase diagram is analysed using Monte Carlo computer simulations and also with density functional theory (DFT), which does not correctly describe the transitions between the different morphologies.

Coarse-graining errors and numerical optimization using a relative entropy framework.

It is shown that the relative entropy offers tight control over the errors due to coarse-graining in arbitrary microscopic properties, and a systematic approach to reducing them is suggested.