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- W2588045278 abstract "Current advances in hardware and algorithm develop- ment allowthe life science researchers to replace the experiment with acomputer simulation. A key ob- ject of these computations is amolecule - a group of atoms interconnected via a cloud ofelectrons. For its computational processing, electrons aroundthe atom are often represented by one number: partial atomiccharge. It can be calculated by quantum mechan- ics (QM), whichoffers high accuracy at the cost of long computation time, ormarkedly faster by empirical methods such as ElectronegativityEqualization Method (EEM). Empirical methods calibrate theirparameters to the particular QM charge calculation approach bymulti-dimensional optimization procedure. This worksystematically summarizes and compares the accuracy andcomputational performance of available EEM pa- rameterizationapproaches with local, global or com- bined optimization (leastsquares, evolutionary and ge- netic algorithms). Moreover, wepropose a new method- ology called guided minimization. Wefound that local optimization plays a crucial role in theparametrization, and only methodologies combining a global anda 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 theresult. Therefore, we reduced the global search method toguided minimization that achieves same or better accuracy thanstate-of-the-art methods and sur- passes them with simplicityand speed." @default.
- W2588045278 created "2017-02-24" @default.
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- W2588045278 date "2016-10-25" @default.
- W2588045278 modified "2023-09-23" @default.
- W2588045278 title "Guided Optimization Method for Fast and Accurate Atomic ChargesComputation" @default.
- W2588045278 hasPublicationYear "2016" @default.
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