Multi-objective particle swarm optimisation for molecular transition state search

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

doi:10.1007/978-3-642-31519-0_27

Multi-objective particle swarm optimisation for molecular transition state search

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-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 language	English
Title of host publication	Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II
Subtitle of host publication	Advances in intelligent systems and computing
Editors	O Schutze, CAC Coello, AA Tantar, E Tantar, P Bouvry, P DelMoral, P Legrand
Publisher	Springer
Pages	415-430
Number of pages	16
Volume	175
ISBN (Print)	978-3-642-31518-3
DOIs	https://doi.org/10.1007/978-3-642-31519-0_27
Publication status	Published - 2012
Externally published	Yes
Event	EVOLVE 2012 International Conference: A bridge between Probability, Set Oriented Numerics and Evolutionary Computation - Mexico City, Mexico City, Mexico Duration: 7 Aug 2012 → 9 Aug 2012 http://evolve.cinvestav.mx/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	SPRINGER-VERLAG BERLIN
Volume	175
ISSN (Print)	2194-5357

Conference

Conference	EVOLVE 2012 International Conference
Country/Territory	Mexico
City	Mexico City
Period	7/08/12 → 9/08/12
Internet address	http://evolve.cinvestav.mx/

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10.1007/978-3-642-31519-0_27

Multi-objective particle swarm optimisation for molecular transition state searchAccepted author manuscript, 237 KBLicence: Unspecified

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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, 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 et al. / 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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title = "Multi-objective particle swarm optimisation for molecular transition state search",

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.",

author = "Jan Hettenhausen and Andrew Lewis and Stephen Chen and Marcus Randall and Rene Fournier",

year = "2012",

doi = "10.1007/978-3-642-31519-0_27",

language = "English",

isbn = "978-3-642-31518-3",

volume = "175",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "415--430",

editor = "O Schutze and CAC Coello and AA Tantar and E Tantar and P Bouvry and P DelMoral and P Legrand",

booktitle = "Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II",

address = "Germany",

note = "EVOLVE 2012 International Conference : A bridge between Probability, Set Oriented Numerics and Evolutionary Computation ; Conference date: 07-08-2012 Through 09-08-2012",

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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 et al.
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 proceeding › Conference contribution › Research › peer-review

TY - GEN

T1 - Multi-objective particle swarm optimisation for molecular transition state search

AU - Hettenhausen, Jan

AU - Lewis, Andrew

AU - Chen, Stephen

AU - Randall, Marcus

AU - Fournier, Rene

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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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T3 - Advances in Intelligent Systems and Computing

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EP - 430

BT - Evolve: A bridge between probability, set oriented numerics, and evolutionary computation II

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A2 - DelMoral, P

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PB - Springer

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ER -

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). doi: 10.1007/978-3-642-31519-0_27

Multi-objective particle swarm optimisation for molecular transition state search

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