Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds
This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release....
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| Published in: | The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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17-11-2024
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| Abstract | This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release. The experimentally observed trends in the slow-to-fast branching ratios of the NO moiety were captured by estimating the branching ratio between the two distinct reaction pathways, viz., roaming and oxaziridine mechanisms, calculated from molecular dynamics simulations performed on a reduced two-dimensional T1 surface. The qualitative agreement between the calculated and experimental results suggests that the mechanism dictating NO release is primarily governed by the dynamics on the T1 surface.This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release. The experimentally observed trends in the slow-to-fast branching ratios of the NO moiety were captured by estimating the branching ratio between the two distinct reaction pathways, viz., roaming and oxaziridine mechanisms, calculated from molecular dynamics simulations performed on a reduced two-dimensional T1 surface. The qualitative agreement between the calculated and experimental results suggests that the mechanism dictating NO release is primarily governed by the dynamics on the T1 surface. |
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| AbstractList | This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release. The experimentally observed trends in the slow-to-fast branching ratios of the NO moiety were captured by estimating the branching ratio between the two distinct reaction pathways, viz., roaming and oxaziridine mechanisms, calculated from molecular dynamics simulations performed on a reduced two-dimensional T1 surface. The qualitative agreement between the calculated and experimental results suggests that the mechanism dictating NO release is primarily governed by the dynamics on the T1 surface.This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release. The experimentally observed trends in the slow-to-fast branching ratios of the NO moiety were captured by estimating the branching ratio between the two distinct reaction pathways, viz., roaming and oxaziridine mechanisms, calculated from molecular dynamics simulations performed on a reduced two-dimensional T1 surface. The qualitative agreement between the calculated and experimental results suggests that the mechanism dictating NO release is primarily governed by the dynamics on the T1 surface. |
| Author | Chowdhury, Prahlad Roy Sharma, Pooja Patwari, G. Naresh Jain, Amber |
| Author_xml | – sequence: 1 givenname: Pooja surname: Sharma fullname: Sharma, Pooja organization: Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India – sequence: 2 givenname: Prahlad Roy orcidid: 0000-0003-0293-2680 surname: Chowdhury fullname: Chowdhury, Prahlad Roy organization: Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India – sequence: 3 givenname: Amber orcidid: 0000-0003-4108-9112 surname: Jain fullname: Jain, Amber organization: Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India – sequence: 4 givenname: G. Naresh orcidid: 0000-0003-0811-7249 surname: Patwari fullname: Patwari, G. Naresh organization: Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India |
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| Title | Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds |
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