272552 Approximating the Solution to the Master Equation to Simulate Directed Self Assembly of Nanostructures
Reliable self assembly of nanoscale systems into a structure of desired geometry will enable various future technological applications such as nanoelectronic circuits and molecular factories . Significant progress has already been made on the self assembly of periodic nanostructures (for example, self-assembled monolayers ). However, less progress has been achieved in fabricating systems with non-periodic nanostructures. Self assembly, being spontaneous, fast and having high spatial resolution, provides a choice for fabricating non-periodic nanostructures. Various interaction forces (electrostatic, Van der Waals, hydrogen bonding, etc.) play a role in self assembly at the nanometer scale . The nanoparticles can interact with each other and with an external force field to form a desired structure . Such a force field can be the result of nanoscale electrodes (or actuators) that are placed on a scaffold, which can be fabricated using various top-down methods such as lithography . The key question is to design the nanoparticles and utilize the external fields to guide self assembly towards a desired structure rapidly and with high probability.
Lakerveld et al.  demonstrated efficient algorithms to solve simultaneously a very large number of master equations that describe the probability of finding the system in a certain configuration at a given time during the course of directed self assembly including parametric sensitivities. However, instances of their model eventually become prohibitively large upon increasing the size of the physical domain as each possible configuration is being considered explicitly.
The aim of this contribution is to demonstrate the application of the Finite State Projection (FSP) method  to simulate directed self assembly. The FSP method is based on simulation of only a subset of the total configuration space (called a projection space). In this work, the projection space is reduced at equal intervals of time. The projection space is also adjusted if the error (caused by simulating only a subset of the total configuration space) exceeds a bound. This adaptation of the projection space is guided by the probability and flux values, and hence it brings the error back within the bound. The adjustment of the projection space is continued until the maximum allowable size of the projection space is reached. This method helps in identifying the time scale over which the system can be simulated in detail and this will depend on the computational resources and the user specified error bounds. It also provides critical information about the dominant configurations and the transition flux between those configurations. A couple of case studies are chosen to demonstrate the method and useful information is revealed about them.
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