Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening

Abstract All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective anode coatings possessing high ionic conductivity and good st...

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Autores principales:	Shreyas J. Honrao, Xin Yang, Balachandran Radhakrishnan, Shigemasa Kuwata, Hideyuki Komatsu, Atsushi Ohma, Maarten Sierhuis, John W. Lawson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/37686c572ef540209ea878aa69aff0be
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spelling	oai:doaj.org-article:37686c572ef540209ea878aa69aff0be2021-12-02T18:51:01ZDiscovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening10.1038/s41598-021-94275-52045-2322https://doaj.org/article/37686c572ef540209ea878aa69aff0be2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94275-5https://doaj.org/toc/2045-2322Abstract All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective anode coatings possessing high ionic conductivity and good stability with Li metal has proven to be a challenge. Here, we present our informatics approach to explore the Li compound space for promising electrolytes and anode coatings using high-throughput multi-property screening and interpretable machine learning. To do this, we generate a database of battery-related materials properties by computing $$\hbox {Li}^+$$ Li + migration barriers and stability windows for over 15,000 Li-containing compounds from Materials Project. We screen through the database for candidates with good thermodynamic and electrochemical stabilities, and low $$\hbox {Li}^+$$ Li + migration barriers, identifying promising new candidates such as $$\hbox {Li}_9\hbox {S}_3$$ Li 9 S 3 N, $$\hbox {LiAlB}_2\hbox {O}_5$$ LiAlB 2 O 5 , $$\hbox {LiYO}_2$$ LiYO 2 , $$\hbox {LiSbF}_4$$ LiSbF 4 , and $$\hbox {Sr}_4\hbox {Li}(\hbox {BN}_2)_3$$ Sr 4 Li ( BN 2 ) 3 , among others. We train machine learning models, using ensemble methods, to predict migration barriers and oxidation and reduction potentials of these compounds by engineering input features that ensure accuracy and interpretability. Using only a small number of features, our gradient boosting regression models achieve $$\mathrm {R}^2$$ R 2 values of 0.95 and 0.92 on the oxidation and reduction potential prediction tasks, respectively, and 0.86 on the migration barrier prediction task. Finally, we use Shapley additive explanations and permutation feature importance analyses to interpret our machine learning predictions and identify materials properties with the largest impact on predictions in our models. We show that our approach has the potential to enable rapid discovery and design of novel solid electrolytes and anode coatings.Shreyas J. HonraoXin YangBalachandran RadhakrishnanShigemasa KuwataHideyuki KomatsuAtsushi OhmaMaarten SierhuisJohn W. LawsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shreyas J. Honrao Xin Yang Balachandran Radhakrishnan Shigemasa Kuwata Hideyuki Komatsu Atsushi Ohma Maarten Sierhuis John W. Lawson Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
description	Abstract All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective anode coatings possessing high ionic conductivity and good stability with Li metal has proven to be a challenge. Here, we present our informatics approach to explore the Li compound space for promising electrolytes and anode coatings using high-throughput multi-property screening and interpretable machine learning. To do this, we generate a database of battery-related materials properties by computing $$\hbox {Li}^+$$ Li + migration barriers and stability windows for over 15,000 Li-containing compounds from Materials Project. We screen through the database for candidates with good thermodynamic and electrochemical stabilities, and low $$\hbox {Li}^+$$ Li + migration barriers, identifying promising new candidates such as $$\hbox {Li}_9\hbox {S}_3$$ Li 9 S 3 N, $$\hbox {LiAlB}_2\hbox {O}_5$$ LiAlB 2 O 5 , $$\hbox {LiYO}_2$$ LiYO 2 , $$\hbox {LiSbF}_4$$ LiSbF 4 , and $$\hbox {Sr}_4\hbox {Li}(\hbox {BN}_2)_3$$ Sr 4 Li ( BN 2 ) 3 , among others. We train machine learning models, using ensemble methods, to predict migration barriers and oxidation and reduction potentials of these compounds by engineering input features that ensure accuracy and interpretability. Using only a small number of features, our gradient boosting regression models achieve $$\mathrm {R}^2$$ R 2 values of 0.95 and 0.92 on the oxidation and reduction potential prediction tasks, respectively, and 0.86 on the migration barrier prediction task. Finally, we use Shapley additive explanations and permutation feature importance analyses to interpret our machine learning predictions and identify materials properties with the largest impact on predictions in our models. We show that our approach has the potential to enable rapid discovery and design of novel solid electrolytes and anode coatings.
format	article
author	Shreyas J. Honrao Xin Yang Balachandran Radhakrishnan Shigemasa Kuwata Hideyuki Komatsu Atsushi Ohma Maarten Sierhuis John W. Lawson
author_facet	Shreyas J. Honrao Xin Yang Balachandran Radhakrishnan Shigemasa Kuwata Hideyuki Komatsu Atsushi Ohma Maarten Sierhuis John W. Lawson
author_sort	Shreyas J. Honrao
title	Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
title_short	Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
title_full	Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
title_fullStr	Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
title_full_unstemmed	Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening
title_sort	discovery of novel li sse and anode coatings using interpretable machine learning and high-throughput multi-property screening
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/37686c572ef540209ea878aa69aff0be
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Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening

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