Data-Driven Studies of Li-Ion-Battery Materials
Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Crystals |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-4352/9/1/54 |
| _version_ | 1817990436101292032 |
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| author | Steven K. Kauwe Trevor David Rhone Taylor D. Sparks |
| author_facet | Steven K. Kauwe Trevor David Rhone Taylor D. Sparks |
| author_sort | Steven K. Kauwe |
| collection | DOAJ |
| description | Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials. |
| first_indexed | 2024-04-14T00:59:00Z |
| format | Article |
| id | doaj.art-0bdea484a7d34ebaa6071226644abc41 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-04-14T00:59:00Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-0bdea484a7d34ebaa6071226644abc412022-12-22T02:21:28ZengMDPI AGCrystals2073-43522019-01-01915410.3390/cryst9010054cryst9010054Data-Driven Studies of Li-Ion-Battery MaterialsSteven K. Kauwe0Trevor David Rhone1Taylor D. Sparks2Materials Science & Engineering Department, University of Utah, Salt Lake City, UT 84112, USAThe Department of Physics, Harvard University, Cambridge, MA 02138, USAMaterials Science & Engineering Department, University of Utah, Salt Lake City, UT 84112, USABatteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.http://www.mdpi.com/2073-4352/9/1/54battery materialsmachine learningmaterials discovery |
| spellingShingle | Steven K. Kauwe Trevor David Rhone Taylor D. Sparks Data-Driven Studies of Li-Ion-Battery Materials Crystals battery materials machine learning materials discovery |
| title | Data-Driven Studies of Li-Ion-Battery Materials |
| title_full | Data-Driven Studies of Li-Ion-Battery Materials |
| title_fullStr | Data-Driven Studies of Li-Ion-Battery Materials |
| title_full_unstemmed | Data-Driven Studies of Li-Ion-Battery Materials |
| title_short | Data-Driven Studies of Li-Ion-Battery Materials |
| title_sort | data driven studies of li ion battery materials |
| topic | battery materials machine learning materials discovery |
| url | http://www.mdpi.com/2073-4352/9/1/54 |
| work_keys_str_mv | AT stevenkkauwe datadrivenstudiesofliionbatterymaterials AT trevordavidrhone datadrivenstudiesofliionbatterymaterials AT taylordsparks datadrivenstudiesofliionbatterymaterials |