# 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…

## 40 Citations

### The role of pressure in inverse design for assembly.

- ChemistryThe Journal of chemical physics
- 2019

It is demonstrated that relative entropy optimization can be used to discover potentials that self-assemble into targeted cluster morphologies with a prescribed pressure when the iterative simulations are performed in the isothermal-isobaric ensemble.

### Inverse design of simple pair potentials for the self-assembly of complex structures.

- ChemistryThe Journal of chemical physics
- 2018

This approach maximizes the relative information of a target pair distribution function with respect to an ansatz distribution function via an iterative update process, resulting in interaction potentials that are smoother and simpler in real space and therefore likely easier to make.

### Inverse design of equilibrium cluster fluids applied to a physically informed model.

- PhysicsThe Journal of chemical physics
- 2021

This work shows that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters that are largely preserved even as the optimized parameters are perturbed.

### Grand canonical inverse design of multicomponent colloidal crystals.

- Materials ScienceSoft matter
- 2020

This work designs low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting, reinforcing the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity.

### Inverse design of multicomponent assemblies.

- Materials ScienceThe Journal of chemical physics
- 2018

This work extends an inverse design methodology to determine isotropic interactions that promote assembly of targeted multicomponent phases, and applies this extension to design interactions for a variety of binary crystals ranging from compact triangular and square architectures to highly open structures with dodecagonal and octadecagonal motifs.

### Inverse design of charged colloidal particle interactions for self assembly into specified crystal structures.

- Materials ScienceThe Journal of chemical physics
- 2019

This work uses the statistical fluctuations in the bond orientational order parameters to tune the interaction parameters for the bcc structure, while initializing the system in the fluid phase, using the Statistical Physics-inspired Inverse Design algorithm.

### Inverse design of isotropic pair potentials using digital alchemy with a generalized Fourier potential

- Materials ScienceThe European Physical Journal B
- 2021

Advances in synthesizing colloidal nanoparticles with tailored interactions through surface modifications provide vast possibilities to create new materials through self-assembly. Alongside…

### Inverse methods for design of soft materials.

- Materials ScienceThe Journal of chemical physics
- 2020

This work discusses recent advances in inverse methods for design of soft materials that address two challenges: methodological limitations that prevent such approaches from satisfying design constraints and computational challenges that limit the size and complexity of systems that can be addressed.

### Learning to grow: control of materials self-assembly using evolutionary reinforcement learning

- Computer SciencePhysical review. E
- 2020

It is shown that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols, pointing the way to the design of synthesis protocols by artificial intelligence.

### From predictive modelling to machine learning and reverse engineering of colloidal self-assembly

- Materials ScienceNature Materials
- 2021

This Review provides an overview of computational tools and strategies, from simulation methods to machine learning and reverse-engineering approaches, used for the design of soft materials made from self-assembling colloids and nanoparticles.

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