Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms.
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| Title: | Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms. |
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| Authors: | Rolczynski BS; Electronics Science and Technology Division, Code 6800, U.S. Naval Research Laboratory, Washington, DC 20375, USA. brian.rolczynski@nrl.navy.mil., Díaz SA; Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC 20375, USA., Kim YC; Materials Science and Technology Division, Code 6300, U.S. Naval Research Laboratory, Washington, DC 20375, USA., Mathur D; Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, USA., Klein WP; Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC 20375, USA., Medintz IL; Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC 20375, USA., Melinger JS; Electronics Science and Technology Division, Code 6800, U.S. Naval Research Laboratory, Washington, DC 20375, USA. brian.rolczynski@nrl.navy.mil. |
| Source: | Physical chemistry chemical physics : PCCP [Phys Chem Chem Phys] 2023 Feb 01; Vol. 25 (5), pp. 3651-3665. Date of Electronic Publication: 2023 Feb 01. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Royal Society of Chemistry Country of Publication: England NLM ID: 100888160 Publication Model: Electronic Cited Medium: Internet ISSN: 1463-9084 (Electronic) Linking ISSN: 14639076 NLM ISO Abbreviation: Phys Chem Chem Phys Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 36648290 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Rolczynski+BS%22">Rolczynski BS</searchLink>; Electronics Science and Technology Division, Code 6800, U.S. Naval Research Laboratory, Washington, DC 20375, USA. brian.rolczynski@nrl.navy.mil.<br /><searchLink fieldCode="AU" term="%22Díaz+SA%22">Díaz SA</searchLink>; Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC 20375, USA.<br /><searchLink fieldCode="AU" term="%22Kim+YC%22">Kim YC</searchLink>; Materials Science and Technology Division, Code 6300, U.S. Naval Research Laboratory, Washington, DC 20375, USA.<br /><searchLink fieldCode="AU" term="%22Mathur+D%22">Mathur D</searchLink>; Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, USA.<br /><searchLink fieldCode="AU" term="%22Klein+WP%22">Klein WP</searchLink>; Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC 20375, USA.<br /><searchLink fieldCode="AU" term="%22Medintz+IL%22">Medintz IL</searchLink>; Center for Bio/Molecular Science and Engineering, Code 6900, U.S. Naval Research Laboratory, Washington, DC 20375, USA.<br /><searchLink fieldCode="AU" term="%22Melinger+JS%22">Melinger JS</searchLink>; Electronics Science and Technology Division, Code 6800, U.S. Naval Research Laboratory, Washington, DC 20375, USA. brian.rolczynski@nrl.navy.mil. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22100888160%22">Physical chemistry chemical physics : PCCP</searchLink> [Phys Chem Chem Phys] 2023 Feb 01; Vol. 25 (5), pp. 3651-3665. <i>Date of Electronic Publication: </i>2023 Feb 01. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Royal+Society+of+Chemistry%22">Royal Society of Chemistry </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>100888160 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1463-9084 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2214639076%22">14639076 </searchLink><i>NLM ISO Abbreviation: </i>Phys Chem Chem Phys <i>Subsets: </i>MEDLINE; PubMed not MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=36648290 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1039/d2cp04960k Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 3651 Titles: – TitleFull: Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rolczynski BS – PersonEntity: Name: NameFull: Díaz SA – PersonEntity: Name: NameFull: Kim YC – PersonEntity: Name: NameFull: Mathur D – PersonEntity: Name: NameFull: Klein WP – PersonEntity: Name: NameFull: Medintz IL – PersonEntity: Name: NameFull: Melinger JS IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2023 Feb 01 Type: published Y: 2023 Identifiers: – Type: issn-electronic Value: 1463-9084 Numbering: – Type: volume Value: 25 – Type: issue Value: 5 Titles: – TitleFull: Physical chemistry chemical physics : PCCP Type: main |
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