Text mining for processing conditions of solid-state battery electrolyte
The search for safer next-generation lithium ion batteries has motivated development of solid-state electrolytes (SSEs), owing to their wide electrochemical potential window, high ionic conductivity (10−3 to 10−4 S cm−1) and good chemical stability with a wide range of high charge capacity electrode...
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doaj-a291871a80ee4f569cceea46ee3a74722020-12-07T04:14:48ZengElsevierElectrochemistry Communications1388-24812020-12-01121106860Text mining for processing conditions of solid-state battery electrolyteRubayyat Mahbub0Kevin Huang1Zach Jensen2Zachary D. Hood3Jennifer L.M. Rupp4Elsa A. Olivetti5Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USADepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Corresponding author.The search for safer next-generation lithium ion batteries has motivated development of solid-state electrolytes (SSEs), owing to their wide electrochemical potential window, high ionic conductivity (10−3 to 10−4 S cm−1) and good chemical stability with a wide range of high charge capacity electrode materials. Still, optimization of the processing conditions of SSEs without sacrificing the performance of the complete cell assembly remains challenging. Insights extracted from scientific literature can accelerate the optimization of processing protocols of SSEs, yet digesting the information scattered over thousands of journal articles is tedious and time consuming. In this work, we demonstrate the role of text mining to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using machine learning and natural language processing techniques that glean information into the processing of sulfide and oxide-based Li SSEs. We also gain insight on low temperature synthesis of highly potential oxide-based Li garnet electrolytes, notably Li7La3Zr2O12 (LLZO), which can reduce the interface complexities during integration of the SSE into cell assembly. This work demonstrates the use of text and data mining to expedite the development of all-solid-state Li metal batteries by guiding hypotheses during experimental design.http://www.sciencedirect.com/science/article/pii/S1388248120302113Lithium batterySolid-state electrolyteLi7La3Zr2O12Text miningNatural language processingMaterial informatics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rubayyat Mahbub Kevin Huang Zach Jensen Zachary D. Hood Jennifer L.M. Rupp Elsa A. Olivetti |
spellingShingle |
Rubayyat Mahbub Kevin Huang Zach Jensen Zachary D. Hood Jennifer L.M. Rupp Elsa A. Olivetti Text mining for processing conditions of solid-state battery electrolyte Electrochemistry Communications Lithium battery Solid-state electrolyte Li7La3Zr2O12 Text mining Natural language processing Material informatics |
author_facet |
Rubayyat Mahbub Kevin Huang Zach Jensen Zachary D. Hood Jennifer L.M. Rupp Elsa A. Olivetti |
author_sort |
Rubayyat Mahbub |
title |
Text mining for processing conditions of solid-state battery electrolyte |
title_short |
Text mining for processing conditions of solid-state battery electrolyte |
title_full |
Text mining for processing conditions of solid-state battery electrolyte |
title_fullStr |
Text mining for processing conditions of solid-state battery electrolyte |
title_full_unstemmed |
Text mining for processing conditions of solid-state battery electrolyte |
title_sort |
text mining for processing conditions of solid-state battery electrolyte |
publisher |
Elsevier |
series |
Electrochemistry Communications |
issn |
1388-2481 |
publishDate |
2020-12-01 |
description |
The search for safer next-generation lithium ion batteries has motivated development of solid-state electrolytes (SSEs), owing to their wide electrochemical potential window, high ionic conductivity (10−3 to 10−4 S cm−1) and good chemical stability with a wide range of high charge capacity electrode materials. Still, optimization of the processing conditions of SSEs without sacrificing the performance of the complete cell assembly remains challenging. Insights extracted from scientific literature can accelerate the optimization of processing protocols of SSEs, yet digesting the information scattered over thousands of journal articles is tedious and time consuming. In this work, we demonstrate the role of text mining to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using machine learning and natural language processing techniques that glean information into the processing of sulfide and oxide-based Li SSEs. We also gain insight on low temperature synthesis of highly potential oxide-based Li garnet electrolytes, notably Li7La3Zr2O12 (LLZO), which can reduce the interface complexities during integration of the SSE into cell assembly. This work demonstrates the use of text and data mining to expedite the development of all-solid-state Li metal batteries by guiding hypotheses during experimental design. |
topic |
Lithium battery Solid-state electrolyte Li7La3Zr2O12 Text mining Natural language processing Material informatics |
url |
http://www.sciencedirect.com/science/article/pii/S1388248120302113 |
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