A comparison of new methods for generating energy-minimizing configurations of patchy particles

Increasingly complex particles are pushing the limits of traditional simulation techniques used to study self-assembly. In this work, we test the use of a learning-augmented Monte Carlo method for predicting low energy configurations of patchy particles shaped like "Tetris (R)" pieces. We extend this method to compare it against Monte Carlo simulations with cluster moves and introduce a new algorithm-bottom-up building block assembly-for quickly generating ordered configurations of particles with a hierarchy of interaction energies. (C) 2009 American Institute of Physics. [doi:10.1063/1.3223834]