Self-Solvation Energies: Extended Open Database and GNN-based prediction

Fluid Phase Equilibria, Journal Year: 2025, Volume and Issue: unknown, P. 114335 - 114335

Published: Jan. 1, 2025

Language: Английский

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A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 1: Halocarbons

Chemical Engineering Science, Journal Year: 2025, Volume and Issue: unknown, P. 121425 - 121425

Published: March 1, 2025

Language: Английский

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Integrating Solvent Effects into the Prediction of Kinetic Constants Using a COSMO-Based Equation of State

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

While kinetic generators produce thermo-kinetic data for detailed gas-phase models, adapting these models liquid-phase applications poses challenges due to the need solvent-dependent thermodynamic properties. To bridge this gap, solvation energies are used incorporate solvent effects into data. However, such an adaptation depends on calculating of unconventional solutes as free radicals and transition states, which not accessible with classical equations states. address issue, work proposes a flexible framework based equation state that integrates all latest advances model family is called tc-PR EoS. Combined quantum-based continuum (COSMO-RS) through advanced mixing rule, proposed made predictive by employing group contribution methods estimate pure compound input parameters required perform calculations model. These can be calculated closed-shell molecules, radicals, average deviation less than 10% respect benchmark database containing experimental well obtained from QSPR-type correlations. The tc-PR/COSMO-RS able predict activation H-abstraction reactions accuracy approximately 0.2 kcal/mol, offering high-throughput accurate solution integrating in liquid phase.

Language: Английский

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Fluorescence-Based Detection of Picric Acid Using Vortex-Assisted Liquid–Liquid Microextraction: An Innovative Analytical Approach

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1051 - 1051

Published: April 1, 2025

A novel design for vortex-assisted liquid–liquid microextraction (VALLME), combined with spectrofluorimetric determination (FLD), was proposed and successfully tested determining picric acid (PA) in water samples. This fluorescence method is based on the formation of an ion associate (IA) through electrostatic interactions, which serves as analytical species measurement presence basic polymethine dye Astrafloksin (AF). The approach aims to minimize volume extraction phase, aligning principles green chemistry. calibration curve linear from 0.92 11.45 µg L−1, R2 0.9930. LOD 0.40 L−1. Density functional theory (DFT) calculations, supported by analysis van der Waals interionic attraction, helped explain experimentally observed selectivity AF cation picrate compared other selected phenols. Theoretical solubility descriptors IA provided insight into n-amyl acetate phase. VALLME-FLD represents a significant advancement PA determination, characterized high sensitivity, selectivity, procedural simplicity. It minimizes use organic solvents, facilitates direct sample preparation, shortens time. developed applied real

Language: Английский

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Multi-fidelity graph neural networks for predicting toluene/water partition coefficients

Published: Aug. 8, 2024

Accurate prediction of toluene/water partition coefficients neutral species is crucial in drug discovery and separation processes; however, data-driven modeling these remains challenging due to limited available experimental data. To address the limitation data, we apply multi-fidelity learning approaches leveraging a quantum chemical dataset (low fidelity) approximately 9000 entries generated by COSMO-RS an (high about 250 collected from literature. We explore transfer learning, feature-augmented multi-target combination with graph neural networks, validating them on two external datasets: one molecules similar training data (EXT-Zamora) more (EXT-SAMPL9). Our results show that significantly improves predictive accuracy, achieving Root-Mean-Square Error (RMSE) 0.44 logP units for EXT-Zamora, compared RMSE 0.63 single-task models. For EXT-SAMPL9 dataset, achieves 1.02 units, indicating reasonable performance even complex molecular structures. These findings highlight potential leverage improve coefficient predictions challenges posed expect applicability methods used beyond just coefficients.

Language: Английский

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Self-Solvation Energies: Extended Open Database and Gnn-Based Prediction

Published: Jan. 1, 2024

Language: Английский

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