Literature DB >> 18818834

Optimising an artificial neural network for predicting the melting point of ionic liquids.

José S Torrecilla1, Francisco Rodríguez, José L Bravo, Gadi Rothenberg, Kenneth R Seddon, Ignacio López-Martin.   

Abstract

We present an optimised artificial neural network (ANN) model for predicting the melting point of a group of 97 imidazolium salts with varied anions. Each cation and anion in the model is described using molecular descriptors. Our model has a mean prediction error of 1.30%, a regression coefficient of 0.99 and a mean P-value of 0.92. The ANN's prediction performance depends mainly on the anion size. In particular, the prediction error decreases as the anion size increases. The high statistical relevance makes this model a useful tool for predicting the melting points of imidazolium-based ionic liquids.

Entities:  

Year:  2008        PMID: 18818834     DOI: 10.1039/b806367b

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  2 in total

1.  The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors.

Authors:  Shahram Lotfi; Shahin Ahmadi; Parvin Kumar
Journal:  RSC Adv       Date:  2021-10-18       Impact factor: 4.036

2.  Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network.

Authors:  Guo Zhao; Hui Wang; Gang Liu
Journal:  Sensors (Basel)       Date:  2017-07-03       Impact factor: 3.576

  2 in total

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