Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes
Molecular details often dictate the macroscopic properties of materials, yet due to their vastly different length scales, relationships between molecular structure and bulk properties can be difficult to predict a priori, requiring Edisonian optimizations and preventing rational design. Here, we int...
| Main Authors: | , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Chemical Society (ACS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/130474 |
| _version_ | 1826207194571341824 |
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| author | Qiao, Bo Mohapatra, Somesh Lopez, Jeffrey Frank Leverick, Graham M. Tatara, Ryoichi Shibuya, Yoshiki Jiang, Yivan France-Lanord, Arthur Grossman, Jeffrey C. Gómez-Bombarelli, Rafael Johnson, Jeremiah A. Shao-Horn, Yang |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Qiao, Bo Mohapatra, Somesh Lopez, Jeffrey Frank Leverick, Graham M. Tatara, Ryoichi Shibuya, Yoshiki Jiang, Yivan France-Lanord, Arthur Grossman, Jeffrey C. Gómez-Bombarelli, Rafael Johnson, Jeremiah A. Shao-Horn, Yang |
| author_sort | Qiao, Bo |
| collection | MIT |
| description | Molecular details often dictate the macroscopic properties of materials, yet due to their vastly different length scales, relationships between molecular structure and bulk properties can be difficult to predict a priori, requiring Edisonian optimizations and preventing rational design. Here, we introduce an easy-to-execute strategy based on linear free energy relationships (LFERs) that enables quantitative correlation and prediction of how molecular modifications, i.e., substituents, impact the ensemble properties of materials. First, we developed substituent parameters based on inexpensive, DFT-computed energetics of elementary pairwise interactions between a given substituent and other constant components of the material. These substituent parameters were then used as inputs to regression analyses of experimentally measured bulk properties, generating a predictive statistical model. We applied this approach to a widely studied class of electrolyte materials: oligo-ethylene glycol (OEG)-LiTFSI mixtures; the resulting model enables elucidation of fundamental physical principles that govern the properties of these electrolytes and also enables prediction of the properties of novel, improved OEG-LiTFSI-based electrolytes. The framework presented here for using context-specific substituent parameters will potentially enhance the throughput of screening new molecular designs for next-generation energy storage devices and other materials-oriented contexts where classical substituent parameters (e.g., Hammett parameters) may not be available or effective. |
| first_indexed | 2024-09-23T13:45:49Z |
| format | Article |
| id | mit-1721.1/130474 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:45:49Z |
| publishDate | 2021 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1304742022-09-28T15:59:52Z Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes Qiao, Bo Mohapatra, Somesh Lopez, Jeffrey Frank Leverick, Graham M. Tatara, Ryoichi Shibuya, Yoshiki Jiang, Yivan France-Lanord, Arthur Grossman, Jeffrey C. Gómez-Bombarelli, Rafael Johnson, Jeremiah A. Shao-Horn, Yang Massachusetts Institute of Technology. Department of Chemical Engineering Massachusetts Institute of Technology. Department of Materials Science and Engineering Massachusetts Institute of Technology. Research Laboratory of Electronics Molecular details often dictate the macroscopic properties of materials, yet due to their vastly different length scales, relationships between molecular structure and bulk properties can be difficult to predict a priori, requiring Edisonian optimizations and preventing rational design. Here, we introduce an easy-to-execute strategy based on linear free energy relationships (LFERs) that enables quantitative correlation and prediction of how molecular modifications, i.e., substituents, impact the ensemble properties of materials. First, we developed substituent parameters based on inexpensive, DFT-computed energetics of elementary pairwise interactions between a given substituent and other constant components of the material. These substituent parameters were then used as inputs to regression analyses of experimentally measured bulk properties, generating a predictive statistical model. We applied this approach to a widely studied class of electrolyte materials: oligo-ethylene glycol (OEG)-LiTFSI mixtures; the resulting model enables elucidation of fundamental physical principles that govern the properties of these electrolytes and also enables prediction of the properties of novel, improved OEG-LiTFSI-based electrolytes. The framework presented here for using context-specific substituent parameters will potentially enhance the throughput of screening new molecular designs for next-generation energy storage devices and other materials-oriented contexts where classical substituent parameters (e.g., Hammett parameters) may not be available or effective. 2021-04-14T15:38:46Z 2021-04-14T15:38:46Z 2020-06 2020-04 2020-08-07T13:57:13Z Article http://purl.org/eprint/type/JournalArticle 2374-7943 2374-7951 https://hdl.handle.net/1721.1/130474 Qiao, Bo et al. "Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes." ACS Central Science 6, 7 (June 2020): 1115–1128 © 2020 American Chemical Society en http://dx.doi.org/10.1021/acscentsci.0c00475 ACS Central Science Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Qiao, Bo Mohapatra, Somesh Lopez, Jeffrey Frank Leverick, Graham M. Tatara, Ryoichi Shibuya, Yoshiki Jiang, Yivan France-Lanord, Arthur Grossman, Jeffrey C. Gómez-Bombarelli, Rafael Johnson, Jeremiah A. Shao-Horn, Yang Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes |
| title | Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes |
| title_full | Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes |
| title_fullStr | Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes |
| title_full_unstemmed | Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes |
| title_short | Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes |
| title_sort | quantitative mapping of molecular substituents to macroscopic properties enables predictive design of oligoethylene glycol based lithium electrolytes |
| url | https://hdl.handle.net/1721.1/130474 |
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