Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid
Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. However, there is still a lack of an effective tool to guide the development of high-performance electrochromic devices. Here, we design a high-performance electr...
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| Veröffentlicht in: | Journal of materials chemistry. C, Materials for optical and electronic devices Materials for optical and electronic devices, 2023-09, Vol.11 (37), p.12776-12784 |
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| container_end_page | 12784 |
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| container_issue | 37 |
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| container_title | Journal of materials chemistry. C, Materials for optical and electronic devices |
| container_volume | 11 |
| creator | Kong, Sifan Li, Muyun Xiang, Yongqi Wu, Yitong Fan, Zhen Yang, Huan Cai, Qingyue Zhang, Menglong Zhang, Yong Ning, Honglong |
| description | Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. However, there is still a lack of an effective tool to guide the development of high-performance electrochromic devices. Here, we design a high-performance electrochromic device, which is composed of a mixed functional layer synthesized from ammonium metatungstate and iron(
ii
) chloride in a heavy water solvent, with the aid of a multilayer perceptron (MLP) model. We first prepare 25 devices with different concentrations of ammonium metatungstate and iron(
ii
) chloride, and use their transmittance modulation amplitude (Δ
T
) current density data to train an MLP model. Then, this model is further used to guide the experimental fabrication of the best-performing device. The fabricated device exhibits high Δ
T
(74%), rapid response time (
t
c
= 6.5 s and
t
b
= 13.5 s), and long cycling life (>1000), which represents a breakthrough in the field of inorganic all-liquid electrochromic devices. Our study showcases a new paradigm of developing high-performance electrochromic devices by using machine learning.
Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. |
| doi_str_mv | 10.1039/d3tc02522e |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_rsc_p</sourceid><recordid>TN_cdi_rsc_primary_d3tc02522e</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2869634729</sourcerecordid><originalsourceid>FETCH-LOGICAL-c281t-4f7deae50ac663ab4aae2f9a1d560bdb9a0c8ddb0f4355acab63d495f93780283</originalsourceid><addsrcrecordid>eNpdkd9LwzAQx4soOOZefBcCvqhQTZO2ax9lzh8w8WU-l2tybTPaZEvayf4f_1Cjkwnm5ZLL576XyzcIziN6G1Ge30neC8oSxvAoGDGa0HCa8Pj4sGfpaTBxbkX9yqI0S_NR8PkKolEaSYtgtdJ1WA9KoiRKb9H1qoZeGU0qYwmQRtVNuEbrTx1ogQRbFL01orGmU4JI3CqfLcF5AV8FXWe0GjrSYQ_9oGvnA4bKGn11TUTTGut7hQ3Cdkc-_JX9r9iqjX_OWXBSQetw8hvHwfvjfDl7DhdvTy-z-0UoWBb1YVxNJQImFESacihjAGRVDpFMUlrKMgcqMilLWsU8SUBAmXIZ50mV82lGWcbHweVed23NZvDjFyszWO1bFsz_VsrjKcs9dbOnhDXOWayKtVUd2F0R0eLbiOKBL2c_Rsw9fLGHrRMH7s8o_gXGdIo2</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2869634729</pqid></control><display><type>article</type><title>Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid</title><source>Royal Society Of Chemistry Journals 2008-</source><creator>Kong, Sifan ; Li, Muyun ; Xiang, Yongqi ; Wu, Yitong ; Fan, Zhen ; Yang, Huan ; Cai, Qingyue ; Zhang, Menglong ; Zhang, Yong ; Ning, Honglong</creator><creatorcontrib>Kong, Sifan ; Li, Muyun ; Xiang, Yongqi ; Wu, Yitong ; Fan, Zhen ; Yang, Huan ; Cai, Qingyue ; Zhang, Menglong ; Zhang, Yong ; Ning, Honglong</creatorcontrib><description>Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. However, there is still a lack of an effective tool to guide the development of high-performance electrochromic devices. Here, we design a high-performance electrochromic device, which is composed of a mixed functional layer synthesized from ammonium metatungstate and iron(
ii
) chloride in a heavy water solvent, with the aid of a multilayer perceptron (MLP) model. We first prepare 25 devices with different concentrations of ammonium metatungstate and iron(
ii
) chloride, and use their transmittance modulation amplitude (Δ
T
) current density data to train an MLP model. Then, this model is further used to guide the experimental fabrication of the best-performing device. The fabricated device exhibits high Δ
T
(74%), rapid response time (
t
c
= 6.5 s and
t
b
= 13.5 s), and long cycling life (>1000), which represents a breakthrough in the field of inorganic all-liquid electrochromic devices. Our study showcases a new paradigm of developing high-performance electrochromic devices by using machine learning.
Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current.</description><identifier>ISSN: 2050-7526</identifier><identifier>EISSN: 2050-7534</identifier><identifier>DOI: 10.1039/d3tc02522e</identifier><language>eng</language><publisher>Cambridge: Royal Society of Chemistry</publisher><subject>Devices ; Electrochromic cells ; Electrochromism ; Heavy water ; Iron chlorides ; Machine learning ; Multilayer perceptrons ; Transmittance</subject><ispartof>Journal of materials chemistry. C, Materials for optical and electronic devices, 2023-09, Vol.11 (37), p.12776-12784</ispartof><rights>Copyright Royal Society of Chemistry 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c281t-4f7deae50ac663ab4aae2f9a1d560bdb9a0c8ddb0f4355acab63d495f93780283</citedby><cites>FETCH-LOGICAL-c281t-4f7deae50ac663ab4aae2f9a1d560bdb9a0c8ddb0f4355acab63d495f93780283</cites><orcidid>0000-0001-7718-5945 ; 0000-0002-1756-641X ; 0000-0001-9518-5738 ; 0000-0003-0011-1379 ; 0000-0003-2874-4311</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Kong, Sifan</creatorcontrib><creatorcontrib>Li, Muyun</creatorcontrib><creatorcontrib>Xiang, Yongqi</creatorcontrib><creatorcontrib>Wu, Yitong</creatorcontrib><creatorcontrib>Fan, Zhen</creatorcontrib><creatorcontrib>Yang, Huan</creatorcontrib><creatorcontrib>Cai, Qingyue</creatorcontrib><creatorcontrib>Zhang, Menglong</creatorcontrib><creatorcontrib>Zhang, Yong</creatorcontrib><creatorcontrib>Ning, Honglong</creatorcontrib><title>Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid</title><title>Journal of materials chemistry. C, Materials for optical and electronic devices</title><description>Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. However, there is still a lack of an effective tool to guide the development of high-performance electrochromic devices. Here, we design a high-performance electrochromic device, which is composed of a mixed functional layer synthesized from ammonium metatungstate and iron(
ii
) chloride in a heavy water solvent, with the aid of a multilayer perceptron (MLP) model. We first prepare 25 devices with different concentrations of ammonium metatungstate and iron(
ii
) chloride, and use their transmittance modulation amplitude (Δ
T
) current density data to train an MLP model. Then, this model is further used to guide the experimental fabrication of the best-performing device. The fabricated device exhibits high Δ
T
(74%), rapid response time (
t
c
= 6.5 s and
t
b
= 13.5 s), and long cycling life (>1000), which represents a breakthrough in the field of inorganic all-liquid electrochromic devices. Our study showcases a new paradigm of developing high-performance electrochromic devices by using machine learning.
Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current.</description><subject>Devices</subject><subject>Electrochromic cells</subject><subject>Electrochromism</subject><subject>Heavy water</subject><subject>Iron chlorides</subject><subject>Machine learning</subject><subject>Multilayer perceptrons</subject><subject>Transmittance</subject><issn>2050-7526</issn><issn>2050-7534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpdkd9LwzAQx4soOOZefBcCvqhQTZO2ax9lzh8w8WU-l2tybTPaZEvayf4f_1Cjkwnm5ZLL576XyzcIziN6G1Ge30neC8oSxvAoGDGa0HCa8Pj4sGfpaTBxbkX9yqI0S_NR8PkKolEaSYtgtdJ1WA9KoiRKb9H1qoZeGU0qYwmQRtVNuEbrTx1ogQRbFL01orGmU4JI3CqfLcF5AV8FXWe0GjrSYQ_9oGvnA4bKGn11TUTTGut7hQ3Cdkc-_JX9r9iqjX_OWXBSQetw8hvHwfvjfDl7DhdvTy-z-0UoWBb1YVxNJQImFESacihjAGRVDpFMUlrKMgcqMilLWsU8SUBAmXIZ50mV82lGWcbHweVed23NZvDjFyszWO1bFsz_VsrjKcs9dbOnhDXOWayKtVUd2F0R0eLbiOKBL2c_Rsw9fLGHrRMH7s8o_gXGdIo2</recordid><startdate>20230928</startdate><enddate>20230928</enddate><creator>Kong, Sifan</creator><creator>Li, Muyun</creator><creator>Xiang, Yongqi</creator><creator>Wu, Yitong</creator><creator>Fan, Zhen</creator><creator>Yang, Huan</creator><creator>Cai, Qingyue</creator><creator>Zhang, Menglong</creator><creator>Zhang, Yong</creator><creator>Ning, Honglong</creator><general>Royal Society of Chemistry</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-7718-5945</orcidid><orcidid>https://orcid.org/0000-0002-1756-641X</orcidid><orcidid>https://orcid.org/0000-0001-9518-5738</orcidid><orcidid>https://orcid.org/0000-0003-0011-1379</orcidid><orcidid>https://orcid.org/0000-0003-2874-4311</orcidid></search><sort><creationdate>20230928</creationdate><title>Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid</title><author>Kong, Sifan ; Li, Muyun ; Xiang, Yongqi ; Wu, Yitong ; Fan, Zhen ; Yang, Huan ; Cai, Qingyue ; Zhang, Menglong ; Zhang, Yong ; Ning, Honglong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c281t-4f7deae50ac663ab4aae2f9a1d560bdb9a0c8ddb0f4355acab63d495f93780283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Devices</topic><topic>Electrochromic cells</topic><topic>Electrochromism</topic><topic>Heavy water</topic><topic>Iron chlorides</topic><topic>Machine learning</topic><topic>Multilayer perceptrons</topic><topic>Transmittance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kong, Sifan</creatorcontrib><creatorcontrib>Li, Muyun</creatorcontrib><creatorcontrib>Xiang, Yongqi</creatorcontrib><creatorcontrib>Wu, Yitong</creatorcontrib><creatorcontrib>Fan, Zhen</creatorcontrib><creatorcontrib>Yang, Huan</creatorcontrib><creatorcontrib>Cai, Qingyue</creatorcontrib><creatorcontrib>Zhang, Menglong</creatorcontrib><creatorcontrib>Zhang, Yong</creatorcontrib><creatorcontrib>Ning, Honglong</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of materials chemistry. C, Materials for optical and electronic devices</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kong, Sifan</au><au>Li, Muyun</au><au>Xiang, Yongqi</au><au>Wu, Yitong</au><au>Fan, Zhen</au><au>Yang, Huan</au><au>Cai, Qingyue</au><au>Zhang, Menglong</au><au>Zhang, Yong</au><au>Ning, Honglong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid</atitle><jtitle>Journal of materials chemistry. C, Materials for optical and electronic devices</jtitle><date>2023-09-28</date><risdate>2023</risdate><volume>11</volume><issue>37</issue><spage>12776</spage><epage>12784</epage><pages>12776-12784</pages><issn>2050-7526</issn><eissn>2050-7534</eissn><abstract>Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. However, there is still a lack of an effective tool to guide the development of high-performance electrochromic devices. Here, we design a high-performance electrochromic device, which is composed of a mixed functional layer synthesized from ammonium metatungstate and iron(
ii
) chloride in a heavy water solvent, with the aid of a multilayer perceptron (MLP) model. We first prepare 25 devices with different concentrations of ammonium metatungstate and iron(
ii
) chloride, and use their transmittance modulation amplitude (Δ
T
) current density data to train an MLP model. Then, this model is further used to guide the experimental fabrication of the best-performing device. The fabricated device exhibits high Δ
T
(74%), rapid response time (
t
c
= 6.5 s and
t
b
= 13.5 s), and long cycling life (>1000), which represents a breakthrough in the field of inorganic all-liquid electrochromic devices. Our study showcases a new paradigm of developing high-performance electrochromic devices by using machine learning.
Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current.</abstract><cop>Cambridge</cop><pub>Royal Society of Chemistry</pub><doi>10.1039/d3tc02522e</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-7718-5945</orcidid><orcidid>https://orcid.org/0000-0002-1756-641X</orcidid><orcidid>https://orcid.org/0000-0001-9518-5738</orcidid><orcidid>https://orcid.org/0000-0003-0011-1379</orcidid><orcidid>https://orcid.org/0000-0003-2874-4311</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2050-7526 |
| ispartof | Journal of materials chemistry. C, Materials for optical and electronic devices, 2023-09, Vol.11 (37), p.12776-12784 |
| issn | 2050-7526 2050-7534 |
| language | eng |
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| source | Royal Society Of Chemistry Journals 2008- |
| subjects | Devices Electrochromic cells Electrochromism Heavy water Iron chlorides Machine learning Multilayer perceptrons Transmittance |
| title | Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid |
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