Title

Multi-objective particle swarm optimisation for molecular transition state search

Date of this Version

1-1-2012

Document Type

Book Chapter

Publication Details

Citation only

Hettenhausen, J., Lewis, A., Chen, S., Randall., M., & Fournier, R. (2012). Multi-objective particle swarm optimisation for molecular transition state search. In O. Schutze, C.A. Coello Coello, A-A Tantar, P. Bouvry, P Del Moral & P. Legrand (Eds.). EVOLVE - A Bridge Between Probability, Set Orientated Numerics, and Evolutionary Computation II (Advances in Intelligent Systems and Computing, 175) (pp. 415-430). Berlin, Germany: Springer-Verlag

Access the publisher

2012 HERDC submission. FoR code: 010303

© Copyright Springer-Verlag, 2013

ISBN

9783642315183

Abstract

This paper describes a novel problem formulation and specialised Multi-Objective Particle Swarm Optimisation (MOPS@) algorithm to discover the reaction pathway and Transition State (TS) of small molecules. Transition states play an important role in computational chemistry and their discovery represents one of the big challenges in computational chemistry. This paper presents a novel problem formulation that defines the TS search as a multi-objective optimisation (MOO) problem. A proof of concept of a modified multi-objective particle swarm optimisation algorithm is presented to find solutions to this problem. While still at a prototype stage, the algorithm was able to find solutions in proximity to the actual TS in many cases. The algorithm is demonstrated on a range of molecules with qualitatively different reaction pathways. Based on this evaluation, possible future developments will be discussed.

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