Using Machine Learning to Predict Enthalpy of Solvation

Jaquis, Brandon J.; Li, Ailin; Monnier, Nolan; Sisk, Robert G.; Acree, William E.; Lang, Andrew S. I. D.

doi:10.1007/s10953-019-00867-1

Cited by 19 publications

(22 citation statements)

References 22 publications

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“…The performance of our models is compared to the following quantum chemistry (QM), ML, and GC methods from literature: SMD, 9 COSMO-RS, 12 the solvation free energy ML model by Lim and Jung (MLSolvA), 43 the transfer learning model by Vermeire and Green 44 (transfer learning), the solvation enthalpy ML model by Jacquis et al, 92 and the solute parameter GC method from the UFZ-LSER database (UFZ-LSER). 29 The COSMO-RS calculations are performed in-house at the BP86/TZVPD-FINE level of theory using the software COSMOtherm.…”

Section: Prediction Using Existing Methodsmentioning

confidence: 99%

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

Chung

Vermeire

et al. 2022

Preprint

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We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code.

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Section: Prediction Using Existing Methodsmentioning

confidence: 99%

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

Chung

Vermeire

et al. 2022

Preprint

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“…One of the current challenges is to answer the question of whether chemical-physical properties, that often require quantum mechanics (e.g., dipole moments, binding and potential energies, and thermodynamics), can be represented and predicted by ML methods (Hansen et al, 2013(Hansen et al, , 2015Montavon et al, 2013;Faber et al, 2016;Iype and Urolagin, 2019;Jaquis et al, 2019). Several attempts have been made on the topic with some successful examples (Rupp et al, 2012;Faber et al, 2017).…”

Section: Improving Computational and Quantum Chemistrymentioning

confidence: 99%

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2019

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Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

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“…Noteworthy, employing machine learning to predict vaporization enthalpy has been previously proposed in a number of studies before. Nevertheless, the currently available machine learning models are mainly limited to evaluating vaporization enthalpy at a single temperature which is commonly either room temperature [13][14] or normal boiling point [15][16] . In the present study we present machine learning models applicable for evaluating vaporization enthalpy at various temperatures.…”

Section: -Introductionmentioning

confidence: 99%

Evaluation of the Temperature Dependence of Vaporization Enthalpy and its Correlation with Surface Tension by Machine Learning and Predictive Correlations

Alibakhshi¹,

Hartke²

2021

Preprint

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Temperature dependence of vaporization enthalpy is one of the most important thermophysical properties of compounds. In the present study, we theoretically developed relationships applicable to evaluation of vaporization enthalpy of compounds from diverse chemical families for a wide temperature range from melting point to the critical temperature. One outcome of the proposed approach is a relationship describing the correlation between the surface tension and vaporization enthalpy which outperforms the extensively applied Kabo method proposed for the same purpose.<br>

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Using Machine Learning to Predict Enthalpy of Solvation

Cited by 19 publications

References 22 publications

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

Evaluation of the Temperature Dependence of Vaporization Enthalpy and its Correlation with Surface Tension by Machine Learning and Predictive Correlations

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