Machine-Learning Model Prediction of Ionic Liquids Melting Points
Ionic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points...
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/5/2408 |
| _version_ | 1797475690986078208 |
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| author | Zafer Acar Phu Nguyen Kah Chun Lau |
| author_facet | Zafer Acar Phu Nguyen Kah Chun Lau |
| author_sort | Zafer Acar |
| collection | DOAJ |
| description | Ionic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points (<i>T<sub>m</sub></i>) which constrain the operating temperatures of the batteries and exhibit unfavorable transport property. To fine tune the <i>T<sub>m</sub></i> of ILs, a systematic study and accurate prediction of <i>T<sub>m</sub></i> of ILs is highly desirable. However, the <i>T<sub>m</sub></i> of an IL can change considerably depending on the molecular structures of the anion and cation and their combination. Thus, a fine control in <i>T<sub>m</sub></i> of ILs can be challenging. In this study, we employed a deep-learning model to predict the <i>T<sub>m</sub></i> of various ILs that consist of different cation and anion classes. Based on this model, a prediction of the melting point of ILs can be made with a reasonably high accuracy, achieving an <i>R</i><sup>2</sup> score of 0.90 with RMSE of ~32 K, and the <i>T<sub>m</sub></i> of ILs are mostly dictated by some important molecular descriptors, which can be used as a set of useful design rules to fine tune the <i>T<sub>m</sub></i> of ILs. |
| first_indexed | 2024-03-09T20:48:34Z |
| format | Article |
| id | doaj.art-584100adaf34415c9ef8ff256d490ecd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:48:34Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-584100adaf34415c9ef8ff256d490ecd2023-11-23T22:40:14ZengMDPI AGApplied Sciences2076-34172022-02-01125240810.3390/app12052408Machine-Learning Model Prediction of Ionic Liquids Melting PointsZafer Acar0Phu Nguyen1Kah Chun Lau2Department of Physics and Astronomy, California State University, Northridge, Los Angeles, CA 91330, USADepartment of Computer Science, California State University, Northridge, Los Angeles, CA 91330, USADepartment of Physics and Astronomy, California State University, Northridge, Los Angeles, CA 91330, USAIonic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points (<i>T<sub>m</sub></i>) which constrain the operating temperatures of the batteries and exhibit unfavorable transport property. To fine tune the <i>T<sub>m</sub></i> of ILs, a systematic study and accurate prediction of <i>T<sub>m</sub></i> of ILs is highly desirable. However, the <i>T<sub>m</sub></i> of an IL can change considerably depending on the molecular structures of the anion and cation and their combination. Thus, a fine control in <i>T<sub>m</sub></i> of ILs can be challenging. In this study, we employed a deep-learning model to predict the <i>T<sub>m</sub></i> of various ILs that consist of different cation and anion classes. Based on this model, a prediction of the melting point of ILs can be made with a reasonably high accuracy, achieving an <i>R</i><sup>2</sup> score of 0.90 with RMSE of ~32 K, and the <i>T<sub>m</sub></i> of ILs are mostly dictated by some important molecular descriptors, which can be used as a set of useful design rules to fine tune the <i>T<sub>m</sub></i> of ILs.https://www.mdpi.com/2076-3417/12/5/2408ionic liquidsdeep-learningchemoinformaticsmelting points |
| spellingShingle | Zafer Acar Phu Nguyen Kah Chun Lau Machine-Learning Model Prediction of Ionic Liquids Melting Points Applied Sciences ionic liquids deep-learning chemoinformatics melting points |
| title | Machine-Learning Model Prediction of Ionic Liquids Melting Points |
| title_full | Machine-Learning Model Prediction of Ionic Liquids Melting Points |
| title_fullStr | Machine-Learning Model Prediction of Ionic Liquids Melting Points |
| title_full_unstemmed | Machine-Learning Model Prediction of Ionic Liquids Melting Points |
| title_short | Machine-Learning Model Prediction of Ionic Liquids Melting Points |
| title_sort | machine learning model prediction of ionic liquids melting points |
| topic | ionic liquids deep-learning chemoinformatics melting points |
| url | https://www.mdpi.com/2076-3417/12/5/2408 |
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