Multi-objective particle swarm optimisation for molecular transition state search

Jan Hettenhausen, Andrew Lewis, Stephen Chen, Marcus Randall, Rene Fournier

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Abstract

This paper describes a novel problem formulation and specialised Multi-Objective Particle Swarm Optimisation (MOPSO) 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.

Original languageEnglish
Title of host publicationEvolve: A bridge between probability, set oriented numerics, and evolutionary computation II
Subtitle of host publicationAdvances in intelligent systems and computing
EditorsO Schutze, CAC Coello, AA Tantar, E Tantar, P Bouvry, P DelMoral, P Legrand
PublisherSpringer
Pages415-430
Number of pages16
Volume175
ISBN (Print)978-3-642-31518-3
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventEVOLVE 2012 International Conference: A bridge between Probability, Set Oriented Numerics and Evolutionary Computation - Mexico City, Mexico City, Mexico
Duration: 7 Aug 20129 Aug 2012
http://evolve.cinvestav.mx/

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSPRINGER-VERLAG BERLIN
Volume175
ISSN (Print)2194-5357

Conference

ConferenceEVOLVE 2012 International Conference
CountryMexico
CityMexico City
Period7/08/129/08/12
Internet address

Cite this

Hettenhausen, J., Lewis, A., Chen, S., Randall, M., & Fournier, R. (2012). Multi-objective particle swarm optimisation for molecular transition state search. In O. Schutze, CAC. Coello, AA. Tantar, E. Tantar, P. Bouvry, P. DelMoral, & P. Legrand (Eds.), Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II : Advances in intelligent systems and computing (Vol. 175, pp. 415-430). (Advances in Intelligent Systems and Computing; Vol. 175). Springer. https://doi.org/10.1007/978-3-642-31519-0_27
Hettenhausen, Jan ; Lewis, Andrew ; Chen, Stephen ; Randall, Marcus ; Fournier, Rene. / Multi-objective particle swarm optimisation for molecular transition state search. Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II : Advances in intelligent systems and computing. editor / O Schutze ; CAC Coello ; AA Tantar ; E Tantar ; P Bouvry ; P DelMoral ; P Legrand. Vol. 175 Springer, 2012. pp. 415-430 (Advances in Intelligent Systems and Computing).
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abstract = "This paper describes a novel problem formulation and specialised Multi-Objective Particle Swarm Optimisation (MOPSO) 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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Hettenhausen, J, Lewis, A, Chen, S, Randall, M & Fournier, R 2012, Multi-objective particle swarm optimisation for molecular transition state search. in O Schutze, CAC Coello, AA Tantar, E Tantar, P Bouvry, P DelMoral & P Legrand (eds), Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II : Advances in intelligent systems and computing. vol. 175, Advances in Intelligent Systems and Computing, vol. 175, Springer, pp. 415-430, EVOLVE 2012 International Conference, Mexico City, Mexico, 7/08/12. https://doi.org/10.1007/978-3-642-31519-0_27

Multi-objective particle swarm optimisation for molecular transition state search. / Hettenhausen, Jan; Lewis, Andrew; Chen, Stephen; Randall, Marcus; Fournier, Rene.

Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II : Advances in intelligent systems and computing. ed. / O Schutze; CAC Coello; AA Tantar; E Tantar; P Bouvry; P DelMoral; P Legrand. Vol. 175 Springer, 2012. p. 415-430 (Advances in Intelligent Systems and Computing; Vol. 175).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Hettenhausen J, Lewis A, Chen S, Randall M, Fournier R. Multi-objective particle swarm optimisation for molecular transition state search. In Schutze O, Coello CAC, Tantar AA, Tantar E, Bouvry P, DelMoral P, Legrand P, editors, Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II : Advances in intelligent systems and computing. Vol. 175. Springer. 2012. p. 415-430. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-642-31519-0_27