Experimental and Computational Modeling of Microemulsion Phase Behavior

The phase behavior of microemulsions formed in a surfactant-brine-oil system for a chemical Enhanced Oil Recovery (EOR) application is complex and depends on a range of parameters. Phase behavior indicates a surfactant solubilization. Phase behavior tests are simple but time-consuming especially when it involves a wide range of surfactant choices at various concentrations. An efficient and insightful microemulsion formulation via computational simulation can complement phase behavior laboratory test. Computational simulation can predict various surfactant properties, including microemulsion phase behavior. Microemulsion phase behavior can be predicted predominantly using Quantitative Structure-Property Relationship (QSPR) model. QSPR models are empirical and limited to simple pure oil system. Its application domain is limited due to the model cannot be extrapolated beyond reference condition. Meanwhile, there are theoretical models based on physical chemistry of microemulsion that can predict microemulsion phase behavior. These models use microemulsion surface tension and torque concepts as well as with solution of bending rigidity of microemulsion interface with relation to surface solubilization and interface energy.

QSPR modeling of selectivity at infinite dilution of ionic liquids

The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.

Catalytic Performance of Cycloalkyl-Fused Aryliminopyridyl Nickel Complexes toward Ethylene Polymerization by QSPR Modeling

Quantitative structure–property relationship (QSPR) modeling is performed to investigate the role of cycloalkyl-fused rings on the catalytic performance of 46 aryliminopyridyl nickel precatalysts. The catalytic activities for nickel complexes in ethylene polymerization are well-predicted by the obtained 2D-QSPR model, exploring the main contribution from the charge distribution of negatively charged atoms. Comparatively, 3D-QSPR models show better predictive and validation capabilities than that of 2D-QSPR for both catalytic activity (Act.) and the molecular weight of the product (Mw). Three-dimensional contour maps illustrate the predominant effect of a steric field on both catalytic properties; smaller sizes of cycloalkyl-fused rings are favorable to Act.y, whereas they are unfavorable to Mw. This study may provide assistance in the design of a new nickel complex with high catalytic performance.

Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules

Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure–property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS0) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS0 value experimentally determined was gathered from the literature. Then, a standard three-layer feed-forward neural network was defined by using 10 ChemGPS physico-chemical descriptors as input features. The developed ANN showed adequate predictive performances on both of the SC-2 test datasets. Benefits and limitations of ML approaches have been highlighted and discussed, starting from this case-study. The main findings confirmed that ML approaches are an attractive and promising tool to predict logS0; however, many aspects, such as data quality, molecular descriptor computation and selection, and assessment of applicability domain, are crucial but often neglected, and should be carefully considered to improve predictions based on ML.

QSPR MODELS FOR ZETA-POTENTIAL OF NANO-OXIDES PREDICTION

Nano-QSPR modeling often requires considering variety of factors, if neglected, may lead to erroneous result of the study. Frequently, the data turned out to be inaccurate, incomplete, or fragmentary. Obviously, the quality of experimental data directly depends on many factors: laboratory equipment, organization of internal regulations, skills of researchers, and so on. As a result of violations of algorithms and protocols of initial data streams processing – there are errors and distortions of data, that is why performing a solid multistep data-curation process is crucial for such procedures. Data curation procedure was performed and approximately 60% was rejected (due to various errors, incomplete or absent records for physicochemical parameters or conditions of performed experiment), followed up by using zeta-potential value dataset for 37 various sizes nanoparticles of 14 metal oxides for calculation of 1D SiRMS descriptors as well as «liquid drop» model cross-descriptors. An efficient consensus model was built (R2 = 0.88, R2test = 0.81). Predictive power (R2 = 0.84) of the model was tested using an external set of 5 nano-oxides and the possibility of satisfactory zeta-potential prediction was shown. Prediction of zeta-potential value within domain applicability of obtained QSPR model confirmed using a Williams plot. The interpretation of the final model was carried out and it was found that the contribution of descriptors was distributed between individual descriptors and cross-descriptors by 46% and 54% respectively. The contribution 1D SiRMS descriptors was 59%, the second group – 41% (liquid drop model descriptors – 29%, descriptors characterizing the metal atom – 12%). It was found that the most influential parameters are the characteristics that reflect the nature of the oxides. The parameters of electrostatic interactions have the highest contribution.

Simultaneous Optimization of Donor/acceptor Pairs and Device Specifications for Non-Fullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors

<p><a></a><a>In addition to designing new donor (D) and/or acceptor (A) molecules, the optimization of</a><a></a><a> experimental fabrication conditions </a>for the organic solar cells (OSCs) is also a complex, multidimensional challenge, which hasn’t been theoretically explored. Herein, a new framework for simultaneous optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationships (QSPR) model built by machine learning. Combining the <a></a><a>device parameters</a> with<a></a><a> structural and electronic </a>variables, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a huge chemical space containing <a>1,942,785</a> D/A pairs is explored to find potential synergistic ones. Favorable expereimental parameters such as root-mean-square (<i>RMS</i>) and the D/A ratio (<i>DAratio</i>) are further screened by grid search methods. <a></a><a></a><a></a><a>Overall, this study suggests </a>the feasibility to optimize D/A molecule pairs and device specifications simultaneously by enabling better-informed and data-driven techniques and this could facilitate the acceleration of improving OSCs efficiencies.</p>