Multi-objective particle swarm optimisation for molecular transition state search

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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

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2012 HERDC submission. FoR code: 010303

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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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