Literature DB >> 29271202

Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods.

Colin A Grambow1, Adeel Jamal1, Yi-Pei Li1, William H Green1, Judit Zádor2, Yury V Suleimanov3.   

Abstract

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The present joint approach significantly outperforms previous manual and automated transition-state searches - 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Overall, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s).

Entities:  

Year:  2018        PMID: 29271202     DOI: 10.1021/jacs.7b11009

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  3 in total

1.  Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants.

Authors:  Yosuke Sumiya; Yu Harabuchi; Yuuya Nagata; Satoshi Maeda
Journal:  JACS Au       Date:  2022-04-22

2.  Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity.

Authors:  Pei-Lin Kang; Yun-Fei Shi; Cheng Shang; Zhi-Pan Liu
Journal:  Chem Sci       Date:  2022-06-20       Impact factor: 9.969

3.  Mining hydroformylation in complex reaction network via graph theory.

Authors:  Keisuke Takahashi; Maeda Satoshi
Journal:  RSC Adv       Date:  2021-07-01       Impact factor: 4.036

  3 in total

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