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Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms.

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Bibliographic Details
Title: Determining interchromophore effects for energy transport in molecular networks using machine-learning algorithms.
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
Description
ISSN:1463-9084
DOI:10.1039/d2cp04960k