首页|New Machine Learning Study Findings Have Been Published by a Researcher at Unive rsity of Paris Saclay (Machine learning for determination of activity of water a nd activity coefficients of electrolytes in binary solutions)
New Machine Learning Study Findings Have Been Published by a Researcher at Unive rsity of Paris Saclay (Machine learning for determination of activity of water a nd activity coefficients of electrolytes in binary solutions)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting out of Gif-sur-Yvett e, France, by NewsRx editors, research stated, "Activity of water and electrolyt es in aqueous solutions is of utmost importance for multiple industrial applicat ions. However, experimental determination of such values is time-consuming, whil e calculation of activity coefficients using numerical methods is challenging." Financial supporters for this research include Agence Nationale De La Recherche. Our news journalists obtained a quote from the research from University of Paris Saclay: "By training neural networks models on literature data, one could predi ct activity of water and electrolytes easily, without requiring any experiment. In this paper, multiple descriptors (or features) are compared to predict activi ty coefficients of electrolytes and activity of water in electrolyte solutions. A neural network based on the Levenberg-Marquardt algorithm (LM-NN) showed high accuracy to calculate values, despite the small size of the training datasets. B oth activity coefficients of electrolytes and activity of water in electrolyte s olutions can be predicted accurately even on unseen data, using simple descripto rs such as electrolyte concentration, ion sizes and charges. However, some discr epancies were observed due to the lack of representativeness of the training dat aset. This could be solved by selecting training data sets that are similar (e.g . same group of the periodic table) to the unknown values, or by including avail able experimental data for the salt considered."
University of Paris SaclayGif-sur-Yvet teFranceEuropeCyborgsElectrolytesEmerging TechnologiesInorganic Chem icalsMachine Learning
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