Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates
ABSTRACT This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion ef...
Gespeichert in:
| Veröffentlicht in: | International journal of quantum chemistry 2024-11, Vol.124 (21), p.n/a |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | n/a |
|---|---|
| container_issue | 21 |
| container_start_page | |
| container_title | International journal of quantum chemistry |
| container_volume | 124 |
| creator | Tahir, Mudassir Hussain Khan, Naeem‐Ul‐Haq Elhindi, Khalid M. |
| description | ABSTRACT
This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.
Machine learning models are used to predict the power conversion efficiency (PCE). Nearly 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has visualized and analyzed. |
| doi_str_mv | 10.1002/qua.27510 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3124097845</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3124097845</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1870-9e0f141eb402b9f0553ce8f93cb9f06b38ab2dd667ab33f7ae087ca03d417b0d3</originalsourceid><addsrcrecordid>eNp1kLFOwzAQhi0EEqUw8AaWmBjSnuMkTseoQEEqAgSV2CzHOYOr1mntVKgbj8Az8iQkhJXp7uzvv5M-Qs4ZjBhAPN7u1CgWKYMDMmAwEVGSsddDMmj_IBIZ5MfkJIQlAGQ8EwOynqFDrxpbO1obeqUaVaqAXf9Yr_Zr9LTQGjdN7QNVrqL3Sr9bh3SOyjvr3r4_v4oQbGiwos_aI3aPXfzaGKstuoZO25ytVIPhlBwZtQp49leHZHFz_TK9jeYPs7tpMY80ywVEEwTDEoZlAnE5MZCmXGNuJlx3U1byXJVxVWWZUCXnRiiEXGgFvEqYKKHiQ3LR7934ervD0MhlvfOuPSk5i5PWS56kLXXZU9rXIXg0cuPtWvm9ZCA7m7K1KX9ttuy4Zz_sCvf_g_JpUfSJH13KeDA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3124097845</pqid></control><display><type>article</type><title>Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates</title><source>Wiley Journals</source><creator>Tahir, Mudassir Hussain ; Khan, Naeem‐Ul‐Haq ; Elhindi, Khalid M.</creator><creatorcontrib>Tahir, Mudassir Hussain ; Khan, Naeem‐Ul‐Haq ; Elhindi, Khalid M.</creatorcontrib><description>ABSTRACT
This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.
Machine learning models are used to predict the power conversion efficiency (PCE). Nearly 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has visualized and analyzed.</description><identifier>ISSN: 0020-7608</identifier><identifier>EISSN: 1097-461X</identifier><identifier>DOI: 10.1002/qua.27510</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Addition polymerization ; Chemical synthesis ; Cluster analysis ; descriptors ; Energy conversion efficiency ; Machine learning ; organic solar cells ; Polymers ; power conversion efficiency ; Predictions</subject><ispartof>International journal of quantum chemistry, 2024-11, Vol.124 (21), p.n/a</ispartof><rights>2024 Wiley Periodicals LLC.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1870-9e0f141eb402b9f0553ce8f93cb9f06b38ab2dd667ab33f7ae087ca03d417b0d3</cites><orcidid>0000-0001-6416-7270</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fqua.27510$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fqua.27510$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Tahir, Mudassir Hussain</creatorcontrib><creatorcontrib>Khan, Naeem‐Ul‐Haq</creatorcontrib><creatorcontrib>Elhindi, Khalid M.</creatorcontrib><title>Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates</title><title>International journal of quantum chemistry</title><description>ABSTRACT
This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.
Machine learning models are used to predict the power conversion efficiency (PCE). Nearly 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has visualized and analyzed.</description><subject>Addition polymerization</subject><subject>Chemical synthesis</subject><subject>Cluster analysis</subject><subject>descriptors</subject><subject>Energy conversion efficiency</subject><subject>Machine learning</subject><subject>organic solar cells</subject><subject>Polymers</subject><subject>power conversion efficiency</subject><subject>Predictions</subject><issn>0020-7608</issn><issn>1097-461X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1kLFOwzAQhi0EEqUw8AaWmBjSnuMkTseoQEEqAgSV2CzHOYOr1mntVKgbj8Az8iQkhJXp7uzvv5M-Qs4ZjBhAPN7u1CgWKYMDMmAwEVGSsddDMmj_IBIZ5MfkJIQlAGQ8EwOynqFDrxpbO1obeqUaVaqAXf9Yr_Zr9LTQGjdN7QNVrqL3Sr9bh3SOyjvr3r4_v4oQbGiwos_aI3aPXfzaGKstuoZO25ytVIPhlBwZtQp49leHZHFz_TK9jeYPs7tpMY80ywVEEwTDEoZlAnE5MZCmXGNuJlx3U1byXJVxVWWZUCXnRiiEXGgFvEqYKKHiQ3LR7934ervD0MhlvfOuPSk5i5PWS56kLXXZU9rXIXg0cuPtWvm9ZCA7m7K1KX9ttuy4Zz_sCvf_g_JpUfSJH13KeDA</recordid><startdate>20241105</startdate><enddate>20241105</enddate><creator>Tahir, Mudassir Hussain</creator><creator>Khan, Naeem‐Ul‐Haq</creator><creator>Elhindi, Khalid M.</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-6416-7270</orcidid></search><sort><creationdate>20241105</creationdate><title>Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates</title><author>Tahir, Mudassir Hussain ; Khan, Naeem‐Ul‐Haq ; Elhindi, Khalid M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1870-9e0f141eb402b9f0553ce8f93cb9f06b38ab2dd667ab33f7ae087ca03d417b0d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Addition polymerization</topic><topic>Chemical synthesis</topic><topic>Cluster analysis</topic><topic>descriptors</topic><topic>Energy conversion efficiency</topic><topic>Machine learning</topic><topic>organic solar cells</topic><topic>Polymers</topic><topic>power conversion efficiency</topic><topic>Predictions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tahir, Mudassir Hussain</creatorcontrib><creatorcontrib>Khan, Naeem‐Ul‐Haq</creatorcontrib><creatorcontrib>Elhindi, Khalid M.</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of quantum chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tahir, Mudassir Hussain</au><au>Khan, Naeem‐Ul‐Haq</au><au>Elhindi, Khalid M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates</atitle><jtitle>International journal of quantum chemistry</jtitle><date>2024-11-05</date><risdate>2024</risdate><volume>124</volume><issue>21</issue><epage>n/a</epage><issn>0020-7608</issn><eissn>1097-461X</eissn><abstract>ABSTRACT
This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.
Machine learning models are used to predict the power conversion efficiency (PCE). Nearly 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has visualized and analyzed.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/qua.27510</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-6416-7270</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0020-7608 |
| ispartof | International journal of quantum chemistry, 2024-11, Vol.124 (21), p.n/a |
| issn | 0020-7608 1097-461X |
| language | eng |
| recordid | cdi_proquest_journals_3124097845 |
| source | Wiley Journals |
| subjects | Addition polymerization Chemical synthesis Cluster analysis descriptors Energy conversion efficiency Machine learning organic solar cells Polymers power conversion efficiency Predictions |
| title | Generation of Database of Polymer Acceptors and Machine Learning‐Assisted Screening of Efficient Candidates |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T12%3A45%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Generation%20of%20Database%20of%20Polymer%20Acceptors%20and%20Machine%20Learning%E2%80%90Assisted%20Screening%20of%20Efficient%20Candidates&rft.jtitle=International%20journal%20of%20quantum%20chemistry&rft.au=Tahir,%20Mudassir%20Hussain&rft.date=2024-11-05&rft.volume=124&rft.issue=21&rft.epage=n/a&rft.issn=0020-7608&rft.eissn=1097-461X&rft_id=info:doi/10.1002/qua.27510&rft_dat=%3Cproquest_cross%3E3124097845%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3124097845&rft_id=info:pmid/&rfr_iscdi=true |