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Joy J, Schaefer AJ, Teynor MS, Ess DH. Dynamical Origin of Rebound versus Dissociation Selectivity during Fe-Oxo-Mediated C-H Functionalization Reactions. J Am Chem Soc 2024; 146:2452-2464. [PMID: 38241715 DOI: 10.1021/jacs.3c09891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
The mechanism of catalytic C-H functionalization of alkanes by Fe-oxo complexes is often suggested to involve a hydrogen atom transfer (HAT) step with the formation of a radical-pair intermediate followed by diverging pathways for radical rebound, dissociation, or desaturation. Recently, we showed that in some Fe-oxo reactions, the radical pair is a nonstatistical-type intermediate and dynamic effects control rebound versus dissociation pathway selectivity. However, the effect of the solvent cage on the stability and lifetime of the radical-pair intermediate has never been analyzed. Moreover, because of the extreme complexity of motion that occurs during dynamics trajectories, the underlying physical origin of pathway selectivity has not yet been determined. For the reaction between [(TQA_Cl)FeIVO]+ and cyclohexane, here, we report explicit solvent trajectories and machine learning analysis on transition-state sampled features (e.g., vibrational, velocity, and geometric) that identified the transferring hydrogen atom kinetic energy as the most important factor controlling rebound versus nonrebound dynamics trajectories, which provides an explanation for our previously proposed dynamic matching effect in fast rebound trajectories that bypass the radical-pair intermediate. Manual control of the reaction trajectories confirmed the importance of this feature and provides a mechanism to enhance or diminish selectivity for the rebound pathway. This led to a general catalyst design principle and proof-of-principle catalyst design that showcases how to control rebound versus dissociation reaction pathway selectivity.
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Affiliation(s)
- Jyothish Joy
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Anthony J Schaefer
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Matthew S Teynor
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84604, United States
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Murakami T, Ibuki S, Hashimoto Y, Kikuma Y, Takayanagi T. Dynamics study of the post-transition-state-bifurcation process of the (HCOOH)H + → CO + H 3O +/HCO + + H 2O dissociation: application of machine-learning techniques. Phys Chem Chem Phys 2023; 25:14016-14027. [PMID: 37161528 DOI: 10.1039/d3cp00252g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The process of protonated formic acid dissociating from the transition state was studied using ring-polymer molecular dynamics (RPMD), classical MD, and quasi-classical trajectory (QCT) simulations. Temperature had a strong influence on the branching fractions for the HCO+ + H2O and CO + H3O+ dissociation channels. The RPMD and classical MD simulations showed similar behavior, but the QCT dynamics were significantly different owing to the excess energies in the quasi-classical trajectories. Machine-learning analysis identified several key features in the phase information of the vibrational motions at the transition state. We found that the initial configuration and momentum of a hydrogen atom connected to a carbon atom and the shrinking coordinate of the CO bond at the transition state play a role in the dynamics of HCO+ + H2O production.
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Affiliation(s)
- Tatsuhiro Murakami
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama City, Saitama, 338-8570, Japan.
- Department of Materials & Life Sciences, Faculty of Science & Technology, Sophia University, 7-1 Kioicho, Chiyoda-ku, Tokyo, 102-8554, Japan
| | - Shunichi Ibuki
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama City, Saitama, 338-8570, Japan.
| | - Yu Hashimoto
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama City, Saitama, 338-8570, Japan.
| | - Yuya Kikuma
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama City, Saitama, 338-8570, Japan.
| | - Toshiyuki Takayanagi
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama City, Saitama, 338-8570, Japan.
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3
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Carpenter BK. Prediction of Kinetic Product Ratios: Investigation of a Dynamically Controlled Case. J Phys Chem A 2023; 127:224-239. [PMID: 36594780 PMCID: PMC9841574 DOI: 10.1021/acs.jpca.2c08301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Of the various factors influencing kinetically controlled product ratios, the role of nonstatistical dynamics is arguably the least well understood. In this paper, reactions were chosen in which dynamics played a dominant role in product selection, by design. Specifically, the reactions studied were the ring openings of cyclopropylidene to allene and tetramethylcyclopropylidene to tetramethylallene (2,4-dimethylpenta-2,3-diene). Both reactions have intrinsic reaction coordinates that bifurcate symmetrically, leading to products that are enantiomeric once the atoms are uniquely labeled. The question addressed in the study was whether the outcomes─that is, which product well on the potential energy surface was selected─could be predicted from their initial conditions for individual trajectories in quasiclassical dynamics simulations. Hybrid potentials were developed based on cooperative interaction between molecular mechanics and artificial neural networks, trained against data from electronic structure calculations. These potentials allowed simulations of both gas-phase and condensed-phase reactions. The outcome was that, for both reactions, prediction of initial selection of product wells could be made with >95% success from initial conditions of the trajectories in the gas phase. However, when trajectories were run for longer, looking for "final" products for each trajectory, the predictability dropped off dramatically. In the gas-phase simulations, this drop off was caused by trajectories hopping between product wells on the potential energy surface. That behavior could be suppressed in condensed phases, but then new uncertainty was introduced because the intermolecular interactions between solute and bath, necessary to permit intermolecular energy transfer and cooling of the hot initial products, often led to perturbations of the initial directions of trajectories on the potential energy surface. It would consequently appear that a general ability to predict outcomes for reactions in which nonstatistical dynamics dominate remains a challenge even in the age of sophisticated machine-learning capabilities.
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Melville J, Hargis C, Davenport MT, Hamilton RS, Ess DH. Machine Learning Analysis of Dynamic‐Dependent Bond Formation in Trajectories with Consecutive Transition States. J PHYS ORG CHEM 2022. [DOI: 10.1002/poc.4405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jesse Melville
- Department of Chemistry and Biochemistry Brigham Young University Provo Utah USA
| | - Cal Hargis
- Department of Chemistry and Biochemistry Brigham Young University Provo Utah USA
| | - Michael T. Davenport
- Department of Chemistry and Biochemistry Brigham Young University Provo Utah USA
| | - R. Spencer Hamilton
- Department of Chemistry and Biochemistry Brigham Young University Provo Utah USA
| | - Daniel H. Ess
- Department of Chemistry and Biochemistry Brigham Young University Provo Utah USA
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5
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Zhu M, Zheng C. Post-spin crossing dynamics determine the regioselectivity in open-shell singlet biradical recombination. Org Chem Front 2022. [DOI: 10.1039/d1qo01757h] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Comprehensive computational studies reveal unique dynamic effects in a multi-spin-state reaction that determine the regioselectivity of a biradical recombination process.
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Affiliation(s)
- Min Zhu
- State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Lu, Shanghai 200032, China
- School of Physical Science and Technology, ShanghaiTech University, 100 Haike Road, Shanghai 201210, China
| | - Chao Zheng
- State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Lu, Shanghai 200032, China
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Maley SM, Melville J, Yu S, Teynor MS, Carlsen R, Hargis C, Hamilton RS, Grant BO, Ess DH. Machine learning classification of disrotatory IRC and conrotatory non-IRC trajectory motion for cyclopropyl radical ring opening. Phys Chem Chem Phys 2021; 23:12309-12320. [PMID: 34018524 DOI: 10.1039/d1cp00612f] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Quasiclassical trajectory analysis is now a standard tool to analyze non-minimum energy pathway motion of organic reactions. However, due to the large amount of information associated with trajectories, quantitative analysis of the dynamic origin of reaction selectivity is complex. For the electrocyclic ring opening of cyclopropyl radical, more than 4000 trajectories were run showing that allyl radicals are formed through a mixture of disrotatory intrinsic reaction coordinate (IRC) motion as well as conrotatory non-IRC motion. Geometric, vibrational mode, and atomic velocity transition-state features from these trajectories were used for supervised machine learning analysis with classification algorithms. Accuracy >80% with a random forest model enabled quantitative and qualitative assessment of transition-state trajectory features controlling disrotatory IRC versus conrotatory non-IRC motion. This analysis revealed that there are two key vibrational modes where their directional combination provides prediction of IRC versus non-IRC motion.
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Affiliation(s)
- Steven M Maley
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Jesse Melville
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Spencer Yu
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Matthew S Teynor
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Ryan Carlsen
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Cal Hargis
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - R Spencer Hamilton
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Benjamin O Grant
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
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Yang LC, Li X, Zhang SQ, Hong X. Machine learning prediction of hydrogen atom transfer reactivity in photoredox-mediated C–H functionalization. Org Chem Front 2021. [DOI: 10.1039/d1qo01325d] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
DFT-computed structure–activity relationship data and physical organic descriptors create accurate machine learning model for HAT barrier prediction in photoredox-mediated HAT catalysis.
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Affiliation(s)
- Li-Cheng Yang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
| | - Xin Li
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
| | - Shuo-Qing Zhang
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China
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