Discovery of solid-state electrolytes for Na-ion batteries using machine learning

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

doi:10.1016/j.matlet.2023.134848

Discovery of solid-state electrolytes for Na-ion batteries using 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

20 Scopus citations

Abstract

A machine learning predictive model based on Random Forest (RF) algorithm was built using experimental works reported on Na-ion solid electrolytes to discover new potential solid-state electrolytes with high ionic conductivity. The model was used to predict ∼25 K compounds from open materials databases and led to the identification of 4 compounds (NaPb₃, Na₃BiO₃, Na₂MoO₄, NaMoF₆) which were expected to show high ionic conductivity and supported by DFT calculations.

Original language	English
Article number	134848
Journal	Materials Letters
Volume	349
DOIs	https://doi.org/10.1016/j.matlet.2023.134848
State	Published - 15 Oct 2023

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

DFT
Ionic conductivity
Machine learning
Na-ion battery
Solid-state electrolytes

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10.1016/j.matlet.2023.134848

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title = "Discovery of solid-state electrolytes for Na-ion batteries using machine learning",

abstract = "A machine learning predictive model based on Random Forest (RF) algorithm was built using experimental works reported on Na-ion solid electrolytes to discover new potential solid-state electrolytes with high ionic conductivity. The model was used to predict ∼25 K compounds from open materials databases and led to the identification of 4 compounds (NaPb3, Na3BiO3, Na2MoO4, NaMoF6) which were expected to show high ionic conductivity and supported by DFT calculations.",

keywords = "DFT, Ionic conductivity, Machine learning, Na-ion battery, Solid-state electrolytes",

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

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AU - Pereznieto, Santiago

AU - Jaafreh, Russlan

AU - Kim, Jung gu

AU - Hamad, Kotiba

PY - 2023/10/15

Y1 - 2023/10/15

N2 - A machine learning predictive model based on Random Forest (RF) algorithm was built using experimental works reported on Na-ion solid electrolytes to discover new potential solid-state electrolytes with high ionic conductivity. The model was used to predict ∼25 K compounds from open materials databases and led to the identification of 4 compounds (NaPb3, Na3BiO3, Na2MoO4, NaMoF6) which were expected to show high ionic conductivity and supported by DFT calculations.

AB - A machine learning predictive model based on Random Forest (RF) algorithm was built using experimental works reported on Na-ion solid electrolytes to discover new potential solid-state electrolytes with high ionic conductivity. The model was used to predict ∼25 K compounds from open materials databases and led to the identification of 4 compounds (NaPb3, Na3BiO3, Na2MoO4, NaMoF6) which were expected to show high ionic conductivity and supported by DFT calculations.

KW - DFT

KW - Ionic conductivity

KW - Machine learning

KW - Na-ion battery

KW - Solid-state electrolytes

UR - https://www.scopus.com/pages/publications/85164353825

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DO - 10.1016/j.matlet.2023.134848

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VL - 349

JO - Materials Letters

JF - Materials Letters

M1 - 134848

ER -

Discovery of solid-state electrolytes for Na-ion batteries using machine learning

Abstract

UN SDGs

Keywords

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