dorsal/arxiv
View SchemaA genetic algorithm to build diatomic potentials
| Authors | Luiz Fernando Roncaratti, Ricardo Gargano, Geraldo Magela e Silva |
|---|---|
| Categories | |
| ArXiv ID | physics/0511132 |
| URL | https://arxiv.org/abs/physics/0511132 |
Abstract
Several types of numerical and combinatorial optimization algorithms have been used as useful tools to minimize functional forms. Generally, when those forms are non-linear or occur in problems without a specific optimization method, stochastic methods based on search algorithms have shown good results due to its smaller susceptibility to be trapped in a local minimum. Besides that, they can be easily implemented to work with other techniques In this algorithms class, the genetic ones have received special attention because they are a robust optimization tool. An algorithm can be named genetic when it uses some kind of codification to transform a set of possible solutions of a given problem in a population that will evolve subject to operators inspired, or not, by mechanisms of natural selection. In other words, they work with a population of solutions to obtain better solutions in the next generation. To do this, they use only information of cost and prize. In this work, we propose a genetic algorithm optimization technique (GAOT) to fit diatomic potential energy curves.In order to show this method, we obtain the analytical functions of the H2+ and Li2 systems using the ab initio energy calculations as well as Rydberg trial function. These studies show that the quality of the GAOT fitting is comparable to the best optimization techniques recommended to fit diatomic systems. The introduction of this new technique is important because it arises as a new option to fit PES for reactive scattering dynamics.
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"abstract": "Several types of numerical and combinatorial optimization algorithms have\nbeen used as useful tools to minimize functional forms. Generally, when those\nforms are non-linear or occur in problems without a specific optimization\nmethod, stochastic methods based on search algorithms have shown good results\ndue to its smaller susceptibility to be trapped in a local minimum. Besides\nthat, they can be easily implemented to work with other techniques In this\nalgorithms class, the genetic ones have received special attention because they\nare a robust optimization tool. An algorithm can be named genetic when it uses\nsome kind of codification to transform a set of possible solutions of a given\nproblem in a population that will evolve subject to operators inspired, or not,\nby mechanisms of natural selection. In other words, they work with a population\nof solutions to obtain better solutions in the next generation. To do this,\nthey use only information of cost and prize. In this work, we propose a genetic\nalgorithm optimization technique (GAOT) to fit diatomic potential energy\ncurves.In order to show this method, we obtain the analytical functions of the\nH2+ and Li2 systems using the ab initio energy calculations as well as Rydberg\ntrial function. These studies show that the quality of the GAOT fitting is\ncomparable to the best optimization techniques recommended to fit diatomic\nsystems. The introduction of this new technique is important because it arises\nas a new option to fit PES for reactive scattering dynamics.",
"arxiv_id": "physics/0511132",
"authors": [
"Luiz Fernando Roncaratti",
"Ricardo Gargano",
"Geraldo Magela e Silva"
],
"categories": [
"physics.chem-ph",
"physics.comp-ph"
],
"title": "A genetic algorithm to build diatomic potentials",
"url": "https://arxiv.org/abs/physics/0511132"
},
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