A Bayesian Approach to Predict Solubility Parameters
Abstract
Abstract Solubility is a ubiquitous phenomenon in many aspects of material science. While solubility can be determined by considering the cohesive forces in a liquid via the Hansen solubility parameters (HSP), quantitative structure–property relationship models are often used for prediction, notably due to their low computational cost. Here, gpHSP, an interpretable and versatile probabilistic approach to determining HSP, is reported. Our model is based on Gaussian processes, a Bayesian machine learning approach that provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP, including a general solvents set aggregated from the literature, and a polymer set experimentally characterized by this group of authors. In all sets, a high degree of agreement is obtained, surpassing well‐established machine learning methods. The general applicability of gpHSP to miscibility of organic semiconductors, drug compounds, and in general solvents is demonstrated, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies.
- Authors:
-
- Department of Chemistry and Chemical Biology Harvard University Cambridge MA 02138 USA
- Institute of Materials for Electronics and Energy Technology (i-MEET) Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Martensstrasse 7 91058 Erlangen Germany
- Institute of Materials for Electronics and Energy Technology (i-MEET) Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Martensstrasse 7 91058 Erlangen Germany, Bavarian Center for Applied Energy Research (ZAE Bayern) Immerwahrstrasse 2 91058 Erlangen Germany
- Department of Chemistry and Chemical Biology Harvard University Cambridge MA 02138 USA, Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada, Department of Computer Science University of Toronto Toronto Ontario M5S 3H7 Canada
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1469250
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Advanced Theory and Simulations
- Additional Journal Information:
- Journal Name: Advanced Theory and Simulations Journal Volume: 2 Journal Issue: 1; Journal ID: ISSN 2513-0390
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- Germany
- Language:
- English
Citation Formats
Sanchez‐Lengeling, Benjamin, Roch, Loïc M., Perea, José Darío, Langner, Stefan, Brabec, Christoph J., and Aspuru‐Guzik, Alán. A Bayesian Approach to Predict Solubility Parameters. Germany: N. p., 2018.
Web. doi:10.1002/adts.201800069.
Sanchez‐Lengeling, Benjamin, Roch, Loïc M., Perea, José Darío, Langner, Stefan, Brabec, Christoph J., & Aspuru‐Guzik, Alán. A Bayesian Approach to Predict Solubility Parameters. Germany. https://doi.org/10.1002/adts.201800069
Sanchez‐Lengeling, Benjamin, Roch, Loïc M., Perea, José Darío, Langner, Stefan, Brabec, Christoph J., and Aspuru‐Guzik, Alán. Mon .
"A Bayesian Approach to Predict Solubility Parameters". Germany. https://doi.org/10.1002/adts.201800069.
@article{osti_1469250,
title = {A Bayesian Approach to Predict Solubility Parameters},
author = {Sanchez‐Lengeling, Benjamin and Roch, Loïc M. and Perea, José Darío and Langner, Stefan and Brabec, Christoph J. and Aspuru‐Guzik, Alán},
abstractNote = {Abstract Solubility is a ubiquitous phenomenon in many aspects of material science. While solubility can be determined by considering the cohesive forces in a liquid via the Hansen solubility parameters (HSP), quantitative structure–property relationship models are often used for prediction, notably due to their low computational cost. Here, gpHSP, an interpretable and versatile probabilistic approach to determining HSP, is reported. Our model is based on Gaussian processes, a Bayesian machine learning approach that provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP, including a general solvents set aggregated from the literature, and a polymer set experimentally characterized by this group of authors. In all sets, a high degree of agreement is obtained, surpassing well‐established machine learning methods. The general applicability of gpHSP to miscibility of organic semiconductors, drug compounds, and in general solvents is demonstrated, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies.},
doi = {10.1002/adts.201800069},
journal = {Advanced Theory and Simulations},
number = 1,
volume = 2,
place = {Germany},
year = {Mon Sep 10 00:00:00 EDT 2018},
month = {Mon Sep 10 00:00:00 EDT 2018}
}
https://doi.org/10.1002/adts.201800069
Web of Science
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