Machine-learned dynamic disorder of electron transfer coupling
Electron transfer (ET) is a fundamental process in chemistry and biochemistry, and electronic coupling is an important determinant of the rate of ET. However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its...
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Veröffentlicht in: | The Journal of chemical physics 2023-07, Vol.159 (3) |
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description | Electron transfer (ET) is a fundamental process in chemistry and biochemistry, and electronic coupling is an important determinant of the rate of ET. However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm−1. Machine-learning models allow direct study of dynamics of electronic coupling in charge transport with sufficient ensemble trajectories, providing further new insights into charge transporting dynamics. |
doi_str_mv | 10.1063/5.0155377 |
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However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm−1. Machine-learning models allow direct study of dynamics of electronic coupling in charge transport with sufficient ensemble trajectories, providing further new insights into charge transporting dynamics.</description><identifier>ISSN: 0021-9606</identifier><identifier>EISSN: 1089-7690</identifier><identifier>DOI: 10.1063/5.0155377</identifier><identifier>PMID: 37458343</identifier><identifier>CODEN: JCPSA6</identifier><language>eng</language><publisher>United States: American Institute of Physics</publisher><subject>Charge transport ; Electron transfer ; Machine learning ; Molecular dynamics ; Naphthalene ; Translational motion</subject><ispartof>The Journal of chemical physics, 2023-07, Vol.159 (3)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm−1. Machine-learning models allow direct study of dynamics of electronic coupling in charge transport with sufficient ensemble trajectories, providing further new insights into charge transporting dynamics.</description><subject>Charge transport</subject><subject>Electron transfer</subject><subject>Machine learning</subject><subject>Molecular dynamics</subject><subject>Naphthalene</subject><subject>Translational motion</subject><issn>0021-9606</issn><issn>1089-7690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp90E1LwzAYB_AgipvTg19ACl5U6EyaNm0vggzfYOJFzyEvTzTSJjNpD_v2Zmx68CA5hIQf_yf5I3RK8JxgRq-rOSZVRet6D00Jbtq8Zi3eR1OMC5K3DLMJOorxE2NM6qI8RBNal1VDSzpFN89CfVgHeQciONCZXjvRW5VpG33QEDJvMuhADcG7bAjCRZMulR9XnXXvx-jAiC7CyW6fobf7u9fFY758eXha3C5zlaYMOZOaGiyKNLcQWNVKGlU2WGtoQVZamtaISjea1aKUVBPDjJJSQzoUGiilM3SxzV0F_zVCHHhvo4KuEw78GHnR0LYo069Youd_6Kcfg0uv26imJGlt1OVWqeBjDGD4KthehDUnmG9K5RXflZrs2S5xlD3oX_nTYgJXWxCVHcRgvfsn7RtrPX8P</recordid><startdate>20230721</startdate><enddate>20230721</enddate><creator>Wang, Yi-Siang</creator><creator>Wang, Chun-I</creator><creator>Yang, Chou-Hsun</creator><creator>Hsu, Chao-Ping</creator><general>American Institute of Physics</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8914-6230</orcidid><orcidid>https://orcid.org/0000-0002-8088-7072</orcidid><orcidid>https://orcid.org/0000-0002-7187-427X</orcidid></search><sort><creationdate>20230721</creationdate><title>Machine-learned dynamic disorder of electron transfer coupling</title><author>Wang, Yi-Siang ; Wang, Chun-I ; Yang, Chou-Hsun ; Hsu, Chao-Ping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c343t-6bd3f0a23742a0c7cbfc480dde9eb5dbf9fa5d8d67a4b3d1f6fcbbde4b32de333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Charge transport</topic><topic>Electron transfer</topic><topic>Machine learning</topic><topic>Molecular dynamics</topic><topic>Naphthalene</topic><topic>Translational motion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yi-Siang</creatorcontrib><creatorcontrib>Wang, Chun-I</creatorcontrib><creatorcontrib>Yang, Chou-Hsun</creatorcontrib><creatorcontrib>Hsu, Chao-Ping</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>The Journal of chemical physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yi-Siang</au><au>Wang, Chun-I</au><au>Yang, Chou-Hsun</au><au>Hsu, Chao-Ping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine-learned dynamic disorder of electron transfer coupling</atitle><jtitle>The Journal of chemical physics</jtitle><addtitle>J Chem Phys</addtitle><date>2023-07-21</date><risdate>2023</risdate><volume>159</volume><issue>3</issue><issn>0021-9606</issn><eissn>1089-7690</eissn><coden>JCPSA6</coden><abstract>Electron transfer (ET) is a fundamental process in chemistry and biochemistry, and electronic coupling is an important determinant of the rate of ET. However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm−1. 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subjects | Charge transport Electron transfer Machine learning Molecular dynamics Naphthalene Translational motion |
title | Machine-learned dynamic disorder of electron transfer coupling |
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