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@inproceedings{1360275,
author = {Pazúriková, Jana and Křenek, Aleš and Matyska, Luděk},
address = {Ghent, Belgicko},
booktitle = {Proceedings of the 2016 European Simulation and Modelling Conference},
editor = {José Évora-Gómez and José Juan Hernandéz-Cabrera},
keywords = {optimization problem; computational chemistry; atomic charges; local vs. global optimization},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Ghent, Belgicko},
isbn = {978-90-77381-95-3},
pages = {267-274},
publisher = {EUROSIS - ETI},
title = {Guided Optimization Method for Fast and Accurate Atomic Charges Computation},
year = {2016}
}
TY - JOUR
ID - 1360275
AU - Pazúriková, Jana - Křenek, Aleš - Matyska, Luděk
PY - 2016
TI - Guided Optimization Method for Fast and Accurate Atomic Charges Computation
PB - EUROSIS - ETI
CY - Ghent, Belgicko
SN - 9789077381953
KW - optimization problem
KW - computational chemistry
KW - atomic charges
KW - local vs. global optimization
N2 - Current advances in hardware and algorithm develop- ment allow the life science researchers to replace the experiment with a computer simulation. A key ob- ject of these computations is a molecule - a group of atoms interconnected via a cloud of electrons. For its computational processing, electrons around the atom are often represented by one number: partial atomic charge. It can be calculated by quantum mechan- ics (QM), which offers high accuracy at the cost of long computation time, or markedly faster by empirical methods such as Electronegativity Equalization Method (EEM). Empirical methods calibrate their parameters to the particular QM charge calculation approach by multi-dimensional optimization procedure. This work systematically summarizes and compares the accuracy and computational performance of available EEM pa- rameterization approaches with local, global or com- bined optimization (least squares, evolutionary and ge- netic algorithms). Moreover, we propose a new method- ology called guided minimization. We found that local optimization plays a crucial role in the parametrization, and only methodologies combining a global and a lo- cal optimization provide high-quality EEM parameters. Furthermore, we observed that global iterations of evo- lutionary of genetic algorithm often do not contribute to the result. Therefore, we reduced the global search method to guided minimization that achieves same or better accuracy than state-of-the-art methods and sur- passes them with simplicity and speed.
ER -
PAZÚRIKOVÁ, Jana, Aleš KŘENEK and Luděk MATYSKA. Guided Optimization Method for Fast and Accurate Atomic Charges Computation. In José Évora-Gómez and José Juan Hernandéz-Cabrera. \textit{Proceedings of the 2016 European Simulation and Modelling Conference}. Ghent, Belgicko: EUROSIS - ETI, 2016, p.~267-274. ISBN~978-90-77381-95-3.
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