Inverse Design for Self Assembly via On-the-Fly Optimization
arXiv:1609.00851 · doi:10.1063/1.4962754
Abstract
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 cooling from higher temperature. Here, we introduce a molecular dynamics simulation-based, optimization design strategy that iteratively and systematically refines the pair interaction according to the fluid and crystalline structural ensembles encountered during the assembly process. We successfully apply this probabilistic, machine-learning approach to the design of repulsive, isotropic pair potentials that assemble into honeycomb, kagome, square, rectangular, truncated square and truncated hexagonal lattices.
7 pages, 5 figures
References in corpus (12)
- Optimized Interactions for Targeted Self-Assembly: Application to Honeycomb Lattice
- Stability of Quasicrystals Composed of Soft Isotropic Particles
- Perspective: Inverse methods for material design
- Turning Statistical Physics Models Into Materials Design Engines
- Inverse design of simple pairwise interactions with low-coordinated 3D lattice ground states
- Designed Diamond Ground State via Optimized Isotropic Monotonic Pair Potentials
- Equilibrium phase behavior of the square-well linear microphase-forming model
- Assembly of nothing: Equilibrium fluids with designed structured porosity
- Inverse Statistical Mechanics: Probing the Limitations of Isotropic Pair Potentials to Produce Ground-State Structural Extremes
- Dimensionality and design of isotropic interactions that stabilize honeycomb, square, simple cubic, and diamond lattices
- Two-dimensional colloidal fluids exhibiting pattern formation
- Breadth versus depth: Interactions that stabilize particle assemblies to changes in density or temperature
Cited by in corpus (28)
- Perspective: Energy Landscapes for Machine Learning
- Inverse methods for design of soft materials
- Learning to grow: control of material self-assembly using evolutionary reinforcement learning
- Probabilistic inverse design for self assembling materials
- Designing the self-assembly of arbitrary shapes using minimal complexity building blocks
- Inverse Design of Multicomponent Assemblies
- Interactions and design rules for assembly of porous colloidal mesophases
- Inverse Design of Simple Pair Potentials for the Self-Assembly of Complex Structures
- Perspective: How to overcome dynamical density functional theory
- Inverse design of charged colloidal particle interactions for self assembly into specified crystal structures
- Assembly of particle strings via isotropic potentials
- Distinct aggregation patterns and fluid porous phase in a 2D model for colloids with competitive interactions
- Neuroevolutionary learning of particles and protocols for self-assembly
- "Pseudoisomorphs" in liquids with intramolecular degrees of freedom
- Designing Pairwise Interactions that Stabilize Open Crystals: Truncated Square and Truncated Hexagonal Lattices
- Realizable hyperuniform and nonhyperuniform particle configurations with targeted spectral functions via effective pair interactions
- Optimising self-assembly through time-dependent interactions
- Optimization of Non-Equilibrium Self-Assembly Protocols Using Markov State Models
- Design of two-dimensional particle assemblies using isotropic pair interactions with an attractive well
- The Role of Pressure in Inverse Design for Assembly
- BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks
- Bayesian Modelling of Pattern Formation from One Snapshot of Pattern
- Multi-objective optimization for targeted self-assembly among competing polymorphs
- Two-step nucleation in a binary mixture of Patchy Particles
- Metadensity functional learning for classical fluids: Regularizing with pair correlations
- relentless: Transparent, reproducible molecular dynamics simulations for optimization
- Designing binary mixtures of colloidal particles with simple interactions that assemble complex crystals
- Free-Energy Functional Approach to Inverse Problems for Self-Assembly of Three-Dimensional Crystals