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Item type:Publication, Access status: with files , Extension of openCOSMO-RS into a full open-source equation of state: Implementation, parameterization, and benchmarkingThe COSMO-SAC-Phi model developed by Soares et al. extends the COSMO-SAC activity-coefficient framework into a full equation of state by explicitly accounting for pressure effects. In this approach, pure substances and mixtures are represented as pseudo-mixtures consisting of the actual number of moles and an additional pseudo-component that describes free volume, or holes. In this work, we implement this extension within the openCOSMO-RS framework and evaluate it using a large and diverse set of molecules and binary systems. The resulting equation of state includes an extensive open-source parameter set with around 1800 pure-component entries, made freely available to the academic community. The four pure-component parameters were fitted to vapor-pressure and liquid-molar volume data for each substance. Model performance was assessed against two benchmark equation-of-state databases, one for pure compounds and one for binary mixtures, without introducing any binary interaction parameters. The resulting openCOSMO-RS-Phi model reproduces the accuracy of the original COSMO-SAC-Phi formulation while providing a fully open-source and accessible implementation for the scientific community. Beyond its immediate utility, it also establishes a foundation for future development of predictive EoS for electrolyte solutions.Publicationtype: Journal Article22 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The steam barrier as a design constraint in carbon capture: pathways to low-temperature regenerationPoint-source carbon capture is conventionally based on steam stripping to regenerate chemical solvents, creating a dependence on high-grade thermal energy and limiting integration with low-temperature heat sources. Steam-based regeneration is highly effective because it simultaneously supplies heat, promotes CO desorption, and facilitates production of a high-purity CO stream. Nevertheless, the widespread use of steam stripping should not be interpreted as evidence that high-temperature regeneration is the only viable pathway for CO release. In this Perspective, we examine solvent regeneration through the lens of energy quality and temperature-sensitive thermodynamic control variables, highlighting why CO desorption can occur at temperatures substantially below those required for conventional steam stripping. We connect observations across buffer systems, sterically hindered and tertiary amines, thermomorphic solvents, and enzymatically assisted capture to a common underlying mechanism: temperature-dependent acid–base equilibria and phase behavior that enable equilibrium-driven desorption without vaporization. Within this framework, carbonic anhydrase emerges not as a marginal absorber-side additive, but as a kinetic enabler that becomes viable under mild, low-temperature desorption conditions. Viewed through this lens, the steam barrier is revealed as one of multiple design options rather than a physical inevitability, opening systematic pathways toward carbon capture systems compatible with low-grade heat and improved exergy efficiency.Publicationtype: Journal Article77 107 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Importance of conformer diversity over quantity in COSMO-RS-Based LLE predictions: a case study(American Chemical Society, 2026-05-09); ; ; ; Accurate phase equilibrium prediction is vital for chemical process design, particularly for challenging systems involving complex intermolecular interactions. This study examines how conformer selection and sigma profiles affect COSMO-RS predictions of liquid–liquid equilibria (LLE’s), using the tetrahydrofurfuryl alcohol (THFA)–methyl isobutyl ketone (MIBK)–water system as a case study. THFA’s conformational flexibility and the contrasting polarity of the two phases, make this system highly sensitive to conformer effects. Predictions were performed at two levels of theory: TZVP and TZVPD-FINE. The TZVP calculations failed to reproduce the experimentally observed LLE behavior, whereas the TZVPD-FINE calculations showed better agreement. This contrast arises because TZVP favors the lowest-energy, less-polar conformers, while TZVPD-FINE emphasizes more polar, hydrogen-bonding-capable conformers. Although the TZVPD-FINE prediction with the full conformer set clearly improves upon the TZVP result and reproduces the main experimental trends, it should be noted that the predicted organic-phase composition does not fully meet the agreement criterion adopted in this work, as MPD<inf>org</inf> equals 5.27%, slightly above the 5% threshold. The prediction should therefore be regarded as near-threshold rather than fully successful, according to our own definition. This residual deviation may arise from several sources, such as experimental uncertainties in tie-line compositions or uncertainties in the predictions for phase compositions close to the plait point or critical solubility point. Single-conformer predictions showed, for the systems tested, that individual conformers could skew predictions and that only a representative conformer set yields reliable predictions. Comparative predictions for the case study investigated further indicated that predictive accuracy depends more on σ-profile diversity than on the number of conformers used. These findings highlight the importance of the conformer representation in COSMO-RS. For systems governed by subtle changes in interactions between phases of different polarity such as liquid–liquid equilibria, both aspects must be carefully considered to ensure predictive reliability. These insights increase understanding of COSMO-RS and might help toward its correct application in both academic and industrial contexts.Publicationtype: Journal Article27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Fast and accurate prediction of solid–liquid equilibria in type V eutectic systems using openCOSMO-RS(American Chemical Society (ACS), 2026-05-06); ; ; Deep eutectic systems (DESs) have attracted growing interest as tunable and environmentally benign alternatives to conventional solvents. However, their practical deployment depends on accurate knowledge of solid–liquid equilibrium (SLE) phase diagrams, the experimental determination of which is time-consuming and resource-intensive. Consequently, reliable and efficient predictive methods for SLE are essential for large-scale DES screening and design. In this work, the performance of the open-source implementation openCOSMO-RS 24a for predicting SLE in type V eutectic systems is evaluated and benchmarked against the commercial COSMO-RS implementation COSMOtherm. A comprehensive data set comprising 157 type V eutectic systems and nearly 2000 experimental data points was compiled from the literature. Several COSMOtherm configurations were examined, including different software versions, levels of theory, and conformer treatments. Model performance was assessed using average absolute deviations (AAD) and average relative deviations (ARD) of melting temperatures. COSMOtherm 25 with multiple conformers yielded the highest accuracy, while openCOSMO-RS 24a achieved comparable predictive performance with only a modest increase in AAD (≈1 K). Notably, openCOSMO-RS performs the calculations several orders of magnitude faster than COSMOtherm. These results demonstrate that openCOSMO-RS offers an attractive balance between accuracy and computational efficiency, making it a powerful tool for high-throughput SLE screening in deep eutectic system design.Publicationtype: Review Article22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 2: atomic polarizabilities(Elsevier, 2026-01-01); ; ; ; OpenCOSMO-RS is an open-source predictive thermodynamic model that can be applied to a broad range of systems in various chemical and biochemical engineering domains. This study focuses on improving openCOSMO-RS by introducing a new dispersion term based on atomic polarizabilities. We evaluate different methods for processing polarizability data, including scaling and combining it to compute segment-segment dispersion interaction energies, with a focus on halocarbon systems. The results demonstrate that the modified model outperforms our previous method developed in the first part of this work Grigorash et al. (2024), while at the same time requiring fewer adjustable parameters. The approach was applied to a broad dataset of over 50,000 data points, consistently increasing the accuracy across a variety of data types. These findings suggest that atomic polarizability is a valuable descriptor for refining dispersion interactions in predictive thermodynamic models.Publicationtype: Journal Article26 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Accurately predicting solubility curves via a thermodynamic cycle, machine learning, and solvent ensembles(American Chemical Society (ACS), 2025-11-21); ; ; Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essential for drug formulation, synthesis, purification, and crystallization. Hard-to-estimate solubility limits currently hinder the design of new processes, making innovation more expensive. We propose a fast and general method for predicting the solubilities of neutral organic molecules in a wide range of solvents and temperatures. Our method uses a thermodynamic fusion cycle to combine machine learning predictions of the activity coefficient, fusion enthalpy, and melting point temperature. This method was tested on a combined data set with more than 100,000 experimental solubility values, showing better or comparable performance to competing methods on many solubility benchmarks even at elevated temperatures. We also introduce reference ensembling to leverage all available experimental solubilities for a given solute in estimating its solubility in a different solvent. Reference ensembling is also shown to enhance the robustness of models trained directly on solubility data.Publicationtype: Journal Article29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Solvation free energies of anions: from curated reference data to predictive models(American Chemical Society (ACS), 2025-08-27); ; ; ; Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited availability of high-quality reference data poses a challenge in developing accurate, inexpensive computational prediction methods. In this study, we address both issues of data quality and availability. We present three databases and models related to ionic phenomena: (1) 8,241 pKa data points across 8 solvents, (2) 5,536 gas-phase acidities from DLPNO-CCSD(T) QM calculations, and (3) 6,090 solvation free energies of anions across 8 solvents obtained from a thermodynamic cycle. We also report 6,088 solvation free energies of neutral conjugate solutes computed using the COSMO-RS method. The pKa data were obtained from the iBonD database, cleaned, and combined with a separate compilation of trustworthy reference pKa data. Gas-phase acidities were computed for most of the acids present in the pKa corpus. Leveraging these data, we compiled values for solvation free energies of anions. We then trained several graph neural network models, which can be used as an alternative to QM approaches to quickly estimate these properties. The pKa and gas-phase acidity models accept reaction SMILES strings of the acid dissociation as inputs, whereas the solvation energy model accepts the SMILES string of the anion. Our microscopic pKa model achieves good accuracy, with an overall test mean average error of 0.58 units on unseen solutes and 0.59 on the SAMPL7 challenge (the lowest error so far among multisolvent models). Our gas-phase acidity model had mean absolute errors slightly above 2 kcal mol-1 when evaluated against experimental data. The anionic solvation free energy model had mean absolute errors of less than 3 kcal mol-1 in several test evaluations, comparable to (though less reliable than) several widely used QM-based solvation models.Publicationtype: Journal Article68 21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Accurately predicting solubility curves via a thermodynamic cycle, machine learning, and solvent ensembles(American Chemical Society (ACS), 2025-08-06); ; Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essential for drug formulation, synthesis, purification, and crystallization. Hard-to-estimate solubility limits currently hinder the design of new processes, making innovation more expensive. We propose a fast and general method for predicting the solubilities of neutral organic molecules in a wide range of solvents and temperatures. Our method uses a thermodynamic fusion cycle to combine machine learning predictions of the activity coefficient, fusion enthalpy, and melting point temperature. This method was tested on a combined dataset with more than 100,000 experimental solubility values, showing better or comparable performance to competing methods on many solubility benchmarks even at elevated temperatures. We also introduce reference ensembling to leverage all available experimental solubilities for a given solute in estimating its solubility in a different solvent. Reference ensembling is also shown to enhance the robustness of models trained directly on solubility data.Publicationtype: Preprint29 34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , How to crack a SMILES: automatic crosschecked chemical structure resolution across multiple services using MoleculeResolverAbstract: Accurate chemical structure resolution from textual identifiers such as names and CAS RN® is critical for computational modeling in chemistry and related fields. This paper introduces MoleculeResolver, an automated, robust Python-based tool designed to address inconsistencies and inaccuracies commonly encountered when converting chemical identifiers to canonical SMILES strings. MoleculeResolver systematically crosschecks structures retrieved from multiple reputable chemical databases, implements rigorous identifier plausibility checks, standardizes molecular structures, and intelligently selects the most accurate representation based on a unique resolution algorithm. Scientific contribution: Benchmarks across diverse datasets confirm that MoleculeResolver significantly enhances precision, recall, and overall reliability compared to traditional single-source methods, proving its utility as a valuable resource for chemists, data scientists, and researchers engaged in high-quality molecular data analysis and predictive model development.Publicationtype: Journal Article15 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Thermodynamics-informed graph neural networks for phase transition enthalpies(PSE Press, 2025-07); ; Phase transition enthalpies, such as those for fusion, vaporization, and sublimation, are vital for understanding thermodynamic properties and aiding early-stage process design. However, measuring these properties is often time-consuming and costly, leading to increased interest in computational methods for fast and accurate predictions. Graph neural networks (GNNs), known for their ability to learn complex molecular representations, have emerged as state-of-the-art tools for predicting various thermophysical properties. Despite their success, GNNs do not inherently obey thermodynamic laws. In this study, we present a multitask GNN designed to predict vaporization, fusion, and sublimation enthalpies of organic compounds. We modified the loss function of the GNN, accounting for the thermodynamic cycle of the three phase transition enthalpies. To train the model, we digitized the extensive Chickos and Acree compendium, which encompasses 32,023 experimental measurements. Two approaches were explored: soft constraints, which guide the model toward thermodynamic consistency, and hard constraints, which enforce fully consistent predictions. The GNN achieved root mean squared errors (RMSEs) of 19.9 kJ/mol for sublimation, 11.0 kJ/mol for fusion, and 16.5 kJ/mol for vaporization enthalpies on the test set. Soft constraints were found to provide a good balance between accuracy and thermodynamic consistency, whereas hard constraints prioritized fidelity at the expense of predictive performance. When compared to the conventional Joback group contribution method the GNN demonstrated an improved accuracy and applicability range. This work underscores the potential of thermodynamics-informed GNNs for predicting thermodynamic properties accurately while maintaining consistency, paving the way for more reliable and efficient computational approaches.Publicationtype: Journal Article33 22