Biomedical subjects

Lixin Zhan

Publications and source records attributed to Lixin Zhan.

3 recordsLinked to original sources

PubMed2006

Monte Carlo basin paving: an improved global optimization method.

We propose a global optimization procedure, basin paving, which is based on the combination of the optimization strategies behind basin hopping and energy landscape paving. As an example, we describe its application in the protein structure prediction by examining two well-studied peptides, where we have found lower potential energy minima than previously located. We also compare the statistics of the searching trajectories produced by basin paving, basin hopping, and energy landscape paving.

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PubMed2005

Asynchronous multicanonical basin hopping method and its application to cobalt nanoclusters.

The multicanonical basin hopping (MUBH) method, which uses a multicanonical weight in the basin hopping (BH) Monte Carlo method, was found to be very efficient for global optimization of large-scale systems such as Lennard-Jones clusters containing more than 150 atoms. We have implemented an asynchronous parallel version of the MUBH method using the message passing interface (MPI) to take advantage of the full usage of multiprocessors in either a homogeneous or heterogeneous computational environment. Based on the intrinsic properties of the Monte Carlo method, this MPI implementation used the task parallelism to minimize interthread data communication. For a Co nanocluster consisting of N atoms, we have applied the asynchronous multicanonical basin hopping (AMUBH) method (for 181 < N < or = 200), together with BH (for 2 < or = N < 150) and MUBH (for 150 < or = N < or = 180), to search for the molecular configuration of the global energy minimum. AMUBH becomes the only practical computational scheme for locating the energy minimum within realistic computational time for a relatively large cluster.

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PubMed2004

Multicanonical basin hopping: a new global optimization method for complex systems.

We introduce a new optimization algorithm that combines the basin-hopping method, which can be used to efficiently map out an energy landscape associated with minima, with the multicanonical Monte Carlo method, which encourages the system to move out of energy traps during the computation. As an example of implementing the algorithm for the global minimization of a multivariable system, we consider the Lennard-Jones systems containing 150-185 particles, and find that the new algorithm is more efficient than the original basin-hopping method.

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