Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

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.

AJAC: Atomic data calculation tool in Python

In this work, new features and extensions of a currently used online atomic database management system are reported. A multiplatform flexible computation package is added to the present system, to allow the calculation of various atomic radiative and collisional processes, based on simplifying the use of some existing atomic codes adopted from the literature. The interaction between users and data is facilitated by a rather extensive Python graphical user interface working online and could be installed in personal computers of different classes. In particular, this study gives an overview of the use of one model of the package models （i.e., electron impact collisional excitation model）. The accuracy of computing capability of the electron impact collisional excitation in the adopted model, which follows the distorted wave approximation approach, is enhanced by implementing the Dirac R-matrix approximation approach. The validity and utility of this approach are presented through a comparison of the current computed results with earlier available theoretical and experimental results. Finally, the source code is made available under the general public license and being distributed freely in the hope that it will be useful to a wide community of laboratory and astrophysical plasma diagnostics.