AI Article Synopsis
- Robust directed self-assembly of nanoscale structures is crucial for advancing technology, but the process can be hindered by kinetic traps due to the complex energy landscape.
- A dynamic model using a master equation is developed to simulate and analyze the self-assembly process of nanoparticles, providing insights into configuration probabilities and optimization strategies over time.
- An efficient algorithm is introduced to handle large-scale simulations, and case studies show how varying parameters affect self-assembly outcomes, with potential for extending this approach to larger systems.
Article Abstract
Robust directed self-assembly of non-periodic nanoscale structures is a key process that would enable various technological breakthroughs. The dynamic evolution of directed self-assemblies towards structures with desired geometries is governed by the rugged potential energy surface of nanoscale systems, potentially leading the system to kinetic traps. To study such phenomena and to set the framework for the directed self-assembly of nanoparticles towards structures with desired geometries, the development of a dynamic model involving a master equation to simulate the directed self-assembly process is presented. The model describes the probability of each possible configuration of a fixed number of nanoparticles on a domain, including parametric sensitivities that can be used for optimization, as a function of time during self-assembly. An algorithm is presented that solves large-scale instances of the model with linear computational complexity. Case studies illustrate the influence of several degrees of freedom on directed self-assembly. A design approach that systematically decomposes the ergodicity of the system to direct self-assembly of a targeted configuration with high probability is illustrated. The prospects for extending such an approach to larger systems using coarse graining techniques are also discussed.
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| http://dx.doi.org/10.1063/1.4716190 | DOI Listing |
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