2009/06/18 by Livia B. Pártay, Lívia B. Pártay, Pártay, Livia B. +5 · 6 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Physics and Astronomy · #Crystallization and Solubility Studies #Machine Learning in Materials Science #Protein Structure and Dynamics #cond-mat.stat-mech
paper · pdf · doi:10.48550/arxiv.0906.3544
26 pages, 9 figures, 1 table; new results added
arxiv created 2010/07/07 · arxiv updated 2010/07/08
We describe a method to explore the configurational phase space of chemical systems. It is based on the nested sampling algorithm recently proposed by Skilling [Skilling J. (2004) In AIP Conference Proceedings, vol. 735, p. 395.; Skilling J. (2006) J of Bayesian Analysis 1:833-860.] and allows us to explore the entire potential energy surface (PES) efficiently in an unbiased way. The algorithm has two parameters which directly control the trade-off between the resolution with which the space is explored and the computational cost. We demonstrate the use of nested sampling on Lennard-Jones (LJ) clusters. Nested sampling provides a straightforward approximation for the partition function, thus evaluating expectation values of arbitrary smooth operators at arbitrary temperatures becomes a simple post-processing step. Access to absolute free energies allows us to determine the temperature-density phase diagram for LJ cluster stability. Even for relatively small clusters, the efficiency gain over parallel tempering in calculating the heat capacity is more than an order of magnitude. Furthermore, by analysing the topology of the resulting samples we are able to visualise the PES in a new and illuminating way. We identify a discretely valued order parameter with basins and supra-basins of the PES allowing a straightforward and unambiguous definition of macroscopic states of an atomistic system and the evaluation of the associated free energies.