In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
This manual describes how to run the new produced GUI C++ program that so called 'WM' program. Section two describes the instructions of the program installation. Section three illustrates test runs description including running the program WM, sample of the input, output files, in addition to some generated graphs followed by the main form of the program created by using the Borland C++ Builder 6.
Free energy barriers associated to the transfer of an excess proton in water and related to the potentials of mean force in proton transfer episodes have been computed in a wide range of thermodynamic states, from low density amorphous ices to high temperature liquids under the critical point for unconstrained and constrained systems. The latter were represented by setups placed inside hydrophobic graphene slabs at the nanometric scale allocating a few water layers, namely one or two in the narrowest case. Waterproton and carbon-proton forces were modelled with a Multi-State Empirical Valence Bond method. As a general trend, a competition between the effects of confinement and temperature is observed on the local hydrogen-bonded structures around the lone proton and, consequently, on the mean force exerted by its environment on the water molecule carrying the proton. Free energy barriers estimated from the computed potentials of mean force tend to rise with the combined effect of increasing temperatures and the packing effect due to a larger extent of hydrophobic confinement. The main reason observed for such enhancement of the free energy barriers was the breaking of the second coordination shell around the lone proton.
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