Machine‐Learning Approaches to Tune Descriptors and Predict the Viscosities of Ionic Liquids and Their Mixtures

PTDC/EQU-EQU/30060/2017 UIDB/50006/2020This work consists on a new chemoinformatic approach based on two complementary artificial intelligence concepts. Random Forest and Kohonen neural network are applied on this context. The former provides a relevance measure of the numerical descriptors encoding either an ionic liquid or its mixtures. The code of a given chemical system is weighted according that relevance measure. The Kohonen neural network is trained with a set of weighted chemical systems. The next step comprises the use of the trained neural network as platform to obtain a tuned profile of numerical descriptors representing a generical chemical system. The tuning mechanism involves the topology of a chemical system‐encoding vector in the neural network. The last step comprises the use of the tuned chemical systems to build predictive models of viscosities. The MOLMAP encoding technology is applied to represent ionic liquid systems and its mixtures.publishersversionpublishe

CO2 + Methanol + Glycerol: Determination of the Compositions in VLLLE From a Synthetic Method-Based Experiment/Theoretical Procedure

The experimental synthetic-based method is a straightforward procedure to obtain a complete resolution of the phase behaviour of VLE of binary mixtures; however, it has limited applicability for multicomponent mixtures/multiphase phenomena. The analytical method is an alternative that offers a reasonable solution for complex configurations, however, the investment of time and resources is high. A straightforward alternative presented in this work consists on the application of the synthetic method allied to a theoretical procedure in order to obtain the composition of the existent phases in equilibrium in complex system’s configurations. The case study is a CO2 + methanol + glycerol mixture that at concrete conditions of global composition, pressure and temperature leads to VLLLE. The composition of each phase in equilibrium was determined using the Peng-Robinson EOS with Mathias-Klotz-Prausnitz mixing rule allied to a combination algorithm to find and check the suitable arrangement of derived compositions that respect the thermodynamic criteria of equilibria, balance of masses and order of densities. A single solution was obtained and discussed along the manuscript.authorsversionpublishe

Solubility of Bioactive, Inorganic and Polymeric Solids in Ionic Liquids — Experimental and Prediction Perspectives

This work was supported by Fundação para a Ciência e a Tecnologia through projects (PEstC/LA0006/2013, PTDC/CTM/103664/2008, EXPL/QEQ ERQ/2243/2013) one contract under Investigador FCT (L.C. Branco); two Postdoctoral fellowships (G.V.S.M. Carrera - SFRH/BPD/72095/2010 and A. V. M. Nunes - SFRH/BPD/74994/2010) and one doctoral fellowship (M. E.Zakrzewska SFRH/BD/74929/2010).publishersversionpublishe

A Chemoinformatic Approach

The combination of the generical molecular maps of atom-level properties (MOLMAPs) encoding approach and the Random Forest algorithm (RF) is applied in order to model, predict, and interpret the structural motifs responsible for a certain organic molecule's melting point (mp) profile. A high-quality database is used for model build-up and evaluation of predictive ability. The obtained results for the complete independent test set (R2 = 0.811, MAE = 31.99 K, RMS = 43.98 K) are comparable or better than reference works. The form of codification represents implicitly the structure of a given molecule and highlights the interactions responsible for a certain melting point profile. This generical encoding approach groups different structural motifs based on its calculated atomic-based properties leading to good predictive ability for structurally different chemical systems not contained in the training set.authorsversionpublishe

Prediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approach

Machine-learning models were developed to predict the composition profile of a three-compound mixture in liquid-liquid equilibrium (LLE), given the global composition at certain temperature and pressure. A chemoinformatics approach was explored, based on the MOLMAP technology to encode molecules and mixtures. The chemical systems involved an ionic liquid (IL) and two organic molecules. Two complementary models have been optimized for the IL-rich and IL-poor phases. The two global optimized models are highly accurate, and were validated with independent test sets, where combinations of molecule1+molecule2+IL are different from those in the training set. These results highlight the MOLMAP encoding scheme, based on atomic properties to train models that learn relationships between features of complex multi-component chemical systems and their profile of phase compositions.authorsversionpublishe

The Solubility of Gases in Ionic Liquids: A ChemoinformaticPredictive and Interpretable Approach

IST-ID/100/2018This work comprises the study of solubilities of gases in ionicliquids (ILs) using a chemoinformatic approach. It is based onthe codification, of the atomic inter-component interactions,cation/gas and anion/gas, which are used to obtain a pattern ofactivation in a Kohonen Neural Network (MOLMAP descriptors). A robust predictive model has been obtained with the Random Forest algorithm and used the maximum proximity as aconfidence measure of a given chemical system compared to the training set. The encoding method has been validated with molecular dynamics. This encoding approach is a valuable estimator of attractive/repulsive interactions of a generical chemical system IL+gas. This method has been used as a fast/visual form of identification of the reasons behind the differences observed between the solubility of CO2and O2in 1-butyl-3-methylimidazolium hexafluorophosphate (BMIM PF6) at identical temperature and pressure (TP) conditions, The effect of variable cation and anion effect has been evaluated.authorsversionpublishe