Solid electrolytes for Li-ion batteries via machine learning

Santiago Pereznieto; Russlan Jaafreh; Jung gu Kim; Kotiba Hamad

doi:10.1016/j.matlet.2023.133926

Solid electrolytes for Li-ion batteries via machine learning

Santiago Pereznieto
, Russlan Jaafreh
, Jung gu Kim
, Kotiba Hamad

Department of Advanced Materials Science and Engineering

Sungkyunkwan University

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

In this work, machine learning (ML) techniques were employed to construct a predictive model that can be used to discover new solid state electrolytes (SE/SSE) for lithium ion batteries (LIBs). The model was built (with R² = 0.97) based on a dataset constructed from previous works regarding ionic conductivity (IC) of solid electrolytes. After a suitable validation process, the ML-model was used to predict the IC of many compositions (∼30 K in Inorganic Crystal Structure Database (ICSD)). Interestingly, the predictions of this model, done on 145 compounds, were consistent with values of Li-phonon band center, which is used as an IC descriptor, this was then used to predict the IC vs temperature behavior of LiYS₂ which is suggested as a promising SSE candidate in this work.

Original language	English
Article number	133926
Journal	Materials Letters
Volume	337
DOIs	https://doi.org/10.1016/j.matlet.2023.133926
State	Published - 15 Apr 2023

Keywords

Ionic conductivity
Li-ion battery
Li-phonon band center
Machine learning
Solid electrolytes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.matlet.2023.133926

Cite this

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title = "Solid electrolytes for Li-ion batteries via machine learning",

abstract = "In this work, machine learning (ML) techniques were employed to construct a predictive model that can be used to discover new solid state electrolytes (SE/SSE) for lithium ion batteries (LIBs). The model was built (with R2 = 0.97) based on a dataset constructed from previous works regarding ionic conductivity (IC) of solid electrolytes. After a suitable validation process, the ML-model was used to predict the IC of many compositions (∼30 K in Inorganic Crystal Structure Database (ICSD)). Interestingly, the predictions of this model, done on 145 compounds, were consistent with values of Li-phonon band center, which is used as an IC descriptor, this was then used to predict the IC vs temperature behavior of LiYS2 which is suggested as a promising SSE candidate in this work.",

keywords = "Ionic conductivity, Li-ion battery, Li-phonon band center, Machine learning, Solid electrolytes",

author = "Santiago Pereznieto and Russlan Jaafreh and Kim, \{Jung gu\} and Kotiba Hamad",

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doi = "10.1016/j.matlet.2023.133926",

language = "English",

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T1 - Solid electrolytes for Li-ion batteries via machine learning

AU - Pereznieto, Santiago

AU - Jaafreh, Russlan

AU - Kim, Jung gu

AU - Hamad, Kotiba

PY - 2023/4/15

Y1 - 2023/4/15

N2 - In this work, machine learning (ML) techniques were employed to construct a predictive model that can be used to discover new solid state electrolytes (SE/SSE) for lithium ion batteries (LIBs). The model was built (with R2 = 0.97) based on a dataset constructed from previous works regarding ionic conductivity (IC) of solid electrolytes. After a suitable validation process, the ML-model was used to predict the IC of many compositions (∼30 K in Inorganic Crystal Structure Database (ICSD)). Interestingly, the predictions of this model, done on 145 compounds, were consistent with values of Li-phonon band center, which is used as an IC descriptor, this was then used to predict the IC vs temperature behavior of LiYS2 which is suggested as a promising SSE candidate in this work.

AB - In this work, machine learning (ML) techniques were employed to construct a predictive model that can be used to discover new solid state electrolytes (SE/SSE) for lithium ion batteries (LIBs). The model was built (with R2 = 0.97) based on a dataset constructed from previous works regarding ionic conductivity (IC) of solid electrolytes. After a suitable validation process, the ML-model was used to predict the IC of many compositions (∼30 K in Inorganic Crystal Structure Database (ICSD)). Interestingly, the predictions of this model, done on 145 compounds, were consistent with values of Li-phonon band center, which is used as an IC descriptor, this was then used to predict the IC vs temperature behavior of LiYS2 which is suggested as a promising SSE candidate in this work.

KW - Ionic conductivity

KW - Li-ion battery

KW - Li-phonon band center

KW - Machine learning

KW - Solid electrolytes

UR - https://www.scopus.com/pages/publications/85147289973

U2 - 10.1016/j.matlet.2023.133926

DO - 10.1016/j.matlet.2023.133926

M3 - Article

AN - SCOPUS:85147289973

SN - 0167-577X

VL - 337

JO - Materials Letters

JF - Materials Letters

M1 - 133926

ER -

Solid electrolytes for Li-ion batteries via machine learning

Abstract

Keywords

UN SDGs

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