Automatized Parameterization of DFTB Using Particle Swarm Optimization

Chien Pin Chou; Yoshifumi Nishimura; Chin Chai Fan; Grzegorz Mazur; Stephan Irle; Henryk A. Witek

doi:10.1021/acs.jctc.5b00673

Automatized Parameterization of DFTB Using Particle Swarm Optimization

Chien Pin Chou
, Yoshifumi Nishimura
, Chin Chai Fan
, Grzegorz Mazur
, Stephan Irle
, Henryk A. Witek

Research output: Contribution to journal › Article › peer-review

60 Scopus citations

Abstract

We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.

Original language	English
Pages (from-to)	53-64
Number of pages	12
Journal	Journal of Chemical Theory and Computation
Volume	12
Issue number	1
DOIs	https://doi.org/10.1021/acs.jctc.5b00673
State	Published - Jan 12 2016
Externally published	Yes

Funding

The Ministry of Science and Technology of Taiwan (MOST 102-2113-M-009-015-MY3, 104-2914-I-009-035-A1) and the Ministry of Education (MOE-ATU project) are acknowledged for financial support.

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10.1021/acs.jctc.5b00673

Cite this

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abstract = "We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.",

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AU - Irle, Stephan

AU - Witek, Henryk A.

PY - 2016/1/12

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AB - We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented.

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Automatized Parameterization of DFTB Using Particle Swarm Optimization

Abstract

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