Diffusion behaviors of lithium ions at the cathode/electrolyte interface from a global neural network potential
The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research. However, the diffusion behaviors at the cathode/electrolyte interface still remain unclear due to the complexity of interfaces. Despite achieving some progress through ab initio molecular...
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creator | Sun, Yufeng Shang, Cheng Fang, Yi-Bin Liu, Zhi-Pan Gong, Xin-Gao Yang, Ji-Hui |
description | The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research. However, the diffusion behaviors at the cathode/electrolyte interface still remain unclear due to the complexity of interfaces. Despite achieving some progress through
ab initio
molecular dynamics (AIMD) and classical molecular dynamics (MD) simulations, a full understanding of Li-ion diffusion behavior requires direct simulation of the entire interface. This remains challenging due to the inherent limitations of current simulation methods. Here, we develop a global neural network potential to reveal the Li ion diffusion behaviors at the interface between the LiCoO
2
cathode and liquid electrolytes (EC, DMC and LiPF
6
) by performing long-term molecular dynamics simulations. We identify four kinds of interfacial diffusion behaviors by analyzing the trajectories of Li ions. While the inactive Li ions are immobile, the active Li ions can shuttle between the interface and solution regions, hop between different interfacial sites, or diffuse as they would in pure electrolytes. Among all diffusion behaviors, only the diffusion across the interface can contribute to the effective conductivity and thus the device performance. Based on the above findings, we further study the influence of electrolyte concentration and interfacial compounds on the diffusion of interfacial Li ions. We show that 1 mol L
−1
LiPF
6
has the largest conductivity across the interface, in agreement with the experimental results that 1 mol L
−1
LiPF
6
is the most suitable electrolyte concentration. We further propose that Li
2
O could be used as an interface coating to improve the Li ion conductivity across the interface. Our work provides deep atomic insights into the dynamics of Li ions at the cathode/electrolyte interface and is expected to help the optimization of LIBs.
The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research. |
doi_str_mv | 10.1039/d4ta05530f |
format | Article |
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ab initio
molecular dynamics (AIMD) and classical molecular dynamics (MD) simulations, a full understanding of Li-ion diffusion behavior requires direct simulation of the entire interface. This remains challenging due to the inherent limitations of current simulation methods. Here, we develop a global neural network potential to reveal the Li ion diffusion behaviors at the interface between the LiCoO
2
cathode and liquid electrolytes (EC, DMC and LiPF
6
) by performing long-term molecular dynamics simulations. We identify four kinds of interfacial diffusion behaviors by analyzing the trajectories of Li ions. While the inactive Li ions are immobile, the active Li ions can shuttle between the interface and solution regions, hop between different interfacial sites, or diffuse as they would in pure electrolytes. Among all diffusion behaviors, only the diffusion across the interface can contribute to the effective conductivity and thus the device performance. Based on the above findings, we further study the influence of electrolyte concentration and interfacial compounds on the diffusion of interfacial Li ions. We show that 1 mol L
−1
LiPF
6
has the largest conductivity across the interface, in agreement with the experimental results that 1 mol L
−1
LiPF
6
is the most suitable electrolyte concentration. We further propose that Li
2
O could be used as an interface coating to improve the Li ion conductivity across the interface. Our work provides deep atomic insights into the dynamics of Li ions at the cathode/electrolyte interface and is expected to help the optimization of LIBs.
The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research.</description><identifier>ISSN: 2050-7488</identifier><identifier>EISSN: 2050-7496</identifier><identifier>DOI: 10.1039/d4ta05530f</identifier><language>eng</language><publisher>Cambridge: Royal Society of Chemistry</publisher><subject>Cathodes ; Conductivity ; Diffusion ; Diffusion coating ; Electrolytes ; Interfaces ; Ion diffusion ; Ions ; Lithium ; Lithium oxides ; Lithium-ion batteries ; Molecular dynamics ; Neural networks ; Trajectory analysis</subject><ispartof>Journal of materials chemistry. A, Materials for energy and sustainability, 2024-12, Vol.12 (48), p.3388-33817</ispartof><rights>Copyright Royal Society of Chemistry 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c170t-d9db4950720e0d97ffcc8b0802107f92d105a8d404ea43d2f506746e50d8c3743</cites><orcidid>0000-0001-7486-1514 ; 0009-0009-4927-6810 ; 0000-0002-2906-5217</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Sun, Yufeng</creatorcontrib><creatorcontrib>Shang, Cheng</creatorcontrib><creatorcontrib>Fang, Yi-Bin</creatorcontrib><creatorcontrib>Liu, Zhi-Pan</creatorcontrib><creatorcontrib>Gong, Xin-Gao</creatorcontrib><creatorcontrib>Yang, Ji-Hui</creatorcontrib><title>Diffusion behaviors of lithium ions at the cathode/electrolyte interface from a global neural network potential</title><title>Journal of materials chemistry. A, Materials for energy and sustainability</title><description>The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research. However, the diffusion behaviors at the cathode/electrolyte interface still remain unclear due to the complexity of interfaces. Despite achieving some progress through
ab initio
molecular dynamics (AIMD) and classical molecular dynamics (MD) simulations, a full understanding of Li-ion diffusion behavior requires direct simulation of the entire interface. This remains challenging due to the inherent limitations of current simulation methods. Here, we develop a global neural network potential to reveal the Li ion diffusion behaviors at the interface between the LiCoO
2
cathode and liquid electrolytes (EC, DMC and LiPF
6
) by performing long-term molecular dynamics simulations. We identify four kinds of interfacial diffusion behaviors by analyzing the trajectories of Li ions. While the inactive Li ions are immobile, the active Li ions can shuttle between the interface and solution regions, hop between different interfacial sites, or diffuse as they would in pure electrolytes. Among all diffusion behaviors, only the diffusion across the interface can contribute to the effective conductivity and thus the device performance. Based on the above findings, we further study the influence of electrolyte concentration and interfacial compounds on the diffusion of interfacial Li ions. We show that 1 mol L
−1
LiPF
6
has the largest conductivity across the interface, in agreement with the experimental results that 1 mol L
−1
LiPF
6
is the most suitable electrolyte concentration. We further propose that Li
2
O could be used as an interface coating to improve the Li ion conductivity across the interface. Our work provides deep atomic insights into the dynamics of Li ions at the cathode/electrolyte interface and is expected to help the optimization of LIBs.
The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research.</description><subject>Cathodes</subject><subject>Conductivity</subject><subject>Diffusion</subject><subject>Diffusion coating</subject><subject>Electrolytes</subject><subject>Interfaces</subject><subject>Ion diffusion</subject><subject>Ions</subject><subject>Lithium</subject><subject>Lithium oxides</subject><subject>Lithium-ion batteries</subject><subject>Molecular dynamics</subject><subject>Neural networks</subject><subject>Trajectory analysis</subject><issn>2050-7488</issn><issn>2050-7496</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpFkNFLwzAQh4MoOOZefBcCvgl11zZZm8exORUEX-ZzSZOLzeyamaTK_nvrJvNefgf3cXd8hFyncJ9CLqaaRQmc52DOyCgDDknBxOz81JflJZmEsIGhSoCZECPiltaYPljX0Rob-WWdD9QZ2trY2H5Lh0GgMtLYIFUyNk7jFFtU0bt2H5HaLqI3UiE13m2ppO-tq2VLO-z9IeK38x905yJ20cr2ilwY2Qac_OWYvK0e1oun5OX18Xkxf0lUWkBMtNA1ExyKDBC0KIxRqqyHr7MUCiMynQKXpWbAULJcZ4bDrGAz5KBLlRcsH5Pb496dd589hlhtXO-74WSVpyzjgwyWDdTdkVLeheDRVDtvt9LvqxSqX6fVkq3nB6erAb45wj6oE_fvPP8BxYR0DQ</recordid><startdate>20241210</startdate><enddate>20241210</enddate><creator>Sun, Yufeng</creator><creator>Shang, Cheng</creator><creator>Fang, Yi-Bin</creator><creator>Liu, Zhi-Pan</creator><creator>Gong, Xin-Gao</creator><creator>Yang, Ji-Hui</creator><general>Royal Society of Chemistry</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>JG9</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-7486-1514</orcidid><orcidid>https://orcid.org/0009-0009-4927-6810</orcidid><orcidid>https://orcid.org/0000-0002-2906-5217</orcidid></search><sort><creationdate>20241210</creationdate><title>Diffusion behaviors of lithium ions at the cathode/electrolyte interface from a global neural network potential</title><author>Sun, Yufeng ; Shang, Cheng ; Fang, Yi-Bin ; Liu, Zhi-Pan ; Gong, Xin-Gao ; Yang, Ji-Hui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c170t-d9db4950720e0d97ffcc8b0802107f92d105a8d404ea43d2f506746e50d8c3743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Cathodes</topic><topic>Conductivity</topic><topic>Diffusion</topic><topic>Diffusion coating</topic><topic>Electrolytes</topic><topic>Interfaces</topic><topic>Ion diffusion</topic><topic>Ions</topic><topic>Lithium</topic><topic>Lithium oxides</topic><topic>Lithium-ion batteries</topic><topic>Molecular dynamics</topic><topic>Neural networks</topic><topic>Trajectory analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Yufeng</creatorcontrib><creatorcontrib>Shang, Cheng</creatorcontrib><creatorcontrib>Fang, Yi-Bin</creatorcontrib><creatorcontrib>Liu, Zhi-Pan</creatorcontrib><creatorcontrib>Gong, Xin-Gao</creatorcontrib><creatorcontrib>Yang, Ji-Hui</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Journal of materials chemistry. A, Materials for energy and sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Yufeng</au><au>Shang, Cheng</au><au>Fang, Yi-Bin</au><au>Liu, Zhi-Pan</au><au>Gong, Xin-Gao</au><au>Yang, Ji-Hui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diffusion behaviors of lithium ions at the cathode/electrolyte interface from a global neural network potential</atitle><jtitle>Journal of materials chemistry. A, Materials for energy and sustainability</jtitle><date>2024-12-10</date><risdate>2024</risdate><volume>12</volume><issue>48</issue><spage>3388</spage><epage>33817</epage><pages>3388-33817</pages><issn>2050-7488</issn><eissn>2050-7496</eissn><abstract>The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research. However, the diffusion behaviors at the cathode/electrolyte interface still remain unclear due to the complexity of interfaces. Despite achieving some progress through
ab initio
molecular dynamics (AIMD) and classical molecular dynamics (MD) simulations, a full understanding of Li-ion diffusion behavior requires direct simulation of the entire interface. This remains challenging due to the inherent limitations of current simulation methods. Here, we develop a global neural network potential to reveal the Li ion diffusion behaviors at the interface between the LiCoO
2
cathode and liquid electrolytes (EC, DMC and LiPF
6
) by performing long-term molecular dynamics simulations. We identify four kinds of interfacial diffusion behaviors by analyzing the trajectories of Li ions. While the inactive Li ions are immobile, the active Li ions can shuttle between the interface and solution regions, hop between different interfacial sites, or diffuse as they would in pure electrolytes. Among all diffusion behaviors, only the diffusion across the interface can contribute to the effective conductivity and thus the device performance. Based on the above findings, we further study the influence of electrolyte concentration and interfacial compounds on the diffusion of interfacial Li ions. We show that 1 mol L
−1
LiPF
6
has the largest conductivity across the interface, in agreement with the experimental results that 1 mol L
−1
LiPF
6
is the most suitable electrolyte concentration. We further propose that Li
2
O could be used as an interface coating to improve the Li ion conductivity across the interface. Our work provides deep atomic insights into the dynamics of Li ions at the cathode/electrolyte interface and is expected to help the optimization of LIBs.
The diffusion of Li ions plays a vital role and has been the central topic of the Li-ion battery (LIB) research.</abstract><cop>Cambridge</cop><pub>Royal Society of Chemistry</pub><doi>10.1039/d4ta05530f</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-7486-1514</orcidid><orcidid>https://orcid.org/0009-0009-4927-6810</orcidid><orcidid>https://orcid.org/0000-0002-2906-5217</orcidid></addata></record> |
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source | Royal Society Of Chemistry Journals 2008- |
subjects | Cathodes Conductivity Diffusion Diffusion coating Electrolytes Interfaces Ion diffusion Ions Lithium Lithium oxides Lithium-ion batteries Molecular dynamics Neural networks Trajectory analysis |
title | Diffusion behaviors of lithium ions at the cathode/electrolyte interface from a global neural network potential |
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