Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912237281607680 |
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| author | Hayer, Nicolas Specht, Thomas Arweiler, Justus Gond, Dominik Hasse, Hans Jirasek, Fabian |
| author_facet | Hayer, Nicolas Specht, Thomas Arweiler, Justus Gond, Dominik Hasse, Hans Jirasek, Fabian |
| contents | In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quantifying the pairwise similarity of components, which we achieve by comparing quantum-chemical descriptors of the components, namely $σ$-profiles. The basic idea behind the approach is that mixtures with similar pairs of components will have similar thermodynamic properties. The SBM is trained on a matrix that contains some data for a given property for different binary mixtures; the missing entries are then predicted by the SBM. As an example, we consider the prediction of isothermal activity coefficients at infinite dilution ($γ^\infty_{ij}$) and show that the SBM outperforms the well-established physical methods modified UNIFAC (Dortmund) and COSMO-SAC-dsp. In this case, the matrix is only sparsely occupied, and it is shown that the SBM works also if only a limited number of data for similar mixtures is available. The SBM idea can be transferred to any mixture property and is a powerful tool for generating essential data for many applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04993 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors Hayer, Nicolas Specht, Thomas Arweiler, Justus Gond, Dominik Hasse, Hans Jirasek, Fabian Chemical Physics In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quantifying the pairwise similarity of components, which we achieve by comparing quantum-chemical descriptors of the components, namely $σ$-profiles. The basic idea behind the approach is that mixtures with similar pairs of components will have similar thermodynamic properties. The SBM is trained on a matrix that contains some data for a given property for different binary mixtures; the missing entries are then predicted by the SBM. As an example, we consider the prediction of isothermal activity coefficients at infinite dilution ($γ^\infty_{ij}$) and show that the SBM outperforms the well-established physical methods modified UNIFAC (Dortmund) and COSMO-SAC-dsp. In this case, the matrix is only sparsely occupied, and it is shown that the SBM works also if only a limited number of data for similar mixtures is available. The SBM idea can be transferred to any mixture property and is a powerful tool for generating essential data for many applications. |
| title | Prediction of Activity Coefficients by Similarity-Based Imputation using Quantum-Chemical Descriptors |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2412.04993 |