Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach however has been less than successful, with the best successful approaches utilizing an additional machine learning algorithm to tune the network hyperparameters. However, in many technical fields, there exist established domain knowledge and understanding about the subject matter. In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network. We demonstrate proof-of-concept by developing IL-Net, a furcated network for predicting the properties of ionic liquids, which is a class of complex multi-chemicals entities. Compared to existing state-of-the-art approaches, we show that furcated networks can improve model accuracy by approximately 20-35 , without using additional labeled data. Lastly, we distill two key design principles for furcated networks that can be adapted to other domains.
Most of the existing literature on using neural networks to model complex multi-chemical entities like ILs has been limited to a single property, and typically has a narrow scope in terms of applicable chemicals. For example, viscosity prediction have been reported in the literature, @cite_21 @cite_15 . While many of these earlier models are still considered to be one of the most accurate temperature dependent viscosity models to date, there is typically a narrowly defined set of ILs for which the models is applicable. @cite_15 Therefore, more recent efforts have focused on expanding the scope of these models. @cite_18 . It should also be emphasized that all prior literature have not looked into neural network architecture design and are using the standard feedforward MLP network design, varying only hyperparameters like number of layers and depth of the network.
@cite_15 @cite_21 @cite_18
2163646971 2085231127
Thermophysical and transport properties of ionic liquids are required for the design of processes and products. Yet the experimental data available are scarce and often contradictory. Based on experimental data collected from the literature, group contribution methods were developed for the estimation of viscosity, electrical conductivity, thermal conductivity, refractive index, isobaric expansivity, and isothermal compressibility, of various families of ionic liquids. Using the Stokes–Einstein relation a correlation for the self-diffusion coefficients with the viscosity is also proposed. The results of the proposed models show average absolute relative deviations generally of the same order of the experimental accuracy of the data. They are easy to use and can provide predictions of property values for ionic liquids never previously studied. The group contribution basis of these models will allow them to be extended to new groups of cations and anions as further data became available. V C 2009 American Institute of Chemical Engineers AIChE J, 55: 1274–1290, 2009 This study aimed to develop prediction models for ionic conductivity and viscosity of ionic liquids (ILs) using quantitative structure property relationships (QSPR) coupled with the descriptors of group contribution type. Utilizing this method, the reverse design of ionic liquids should be available by just a very simple iterative calculation. The polynomial expansion model based on the type of cation, length of side chain, and type of anion was applied to the expression of ionic liquid properties. Parameters of these polynomial expansion models were determined by means of a genetic algorithm (GA), one of the most popular chemoinformatics techniques. We also undertook the reverse design of ILs. Using this group contribution base properties estimation method, our reverse design system cyclopaedically generated ILs structures corresponding to particular physical properties.