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Structural Optimization of Nanoclusters with Adaptive Tempering Monte Carlo Method

X. Dong and E. Blaisten-Barojas
George Mason University, US

Keywords:
nanoclusters, multicanonical, tempering, Monte Carlo, optimization, polypeptide

Abstract:
The Adaptive Tempering Monte Carlo (ATMC) method was proposed for the optimization of nanosystems. In the simulation, tempering is applied such that the temperature of the system is changed adaptively such that a multitude of canonical ensembles are linked by a super-Markov chain. The system adaptive excursion in configuration space produced by the ATMC allows for rapid discovery of topological paths on the potential energy surface (PES). The system drives fast towards the global minimum. Additionally we implemented a parallel version of the ATMC in a multi-thread way, such that a group of tempering threads are thrown simultaneously. All threads explore the PES, and if one temperature is repeated, then samplings of the two data segments are appended. In this work the ATMC is applied to different types of nanoclusters, which include crystalization of thin films and nanoclusters under the Lennard Jones potential, Morse nanoclusters, and tight-binding nanoclusters. The ATMC was further applied to characterize the best folded structure of a polypeptide chain. The adaptive tempering successfully drives the protein to the native state from unfolded states. The ATMC is an effective optimization method, and it has shown its versatility with different types of problems in the nanoscience arena.

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