Literature DB >> 29084387

Context-Driven Exploration of Complex Chemical Reaction Networks.

Gregor N Simm1, Markus Reiher1.   

Abstract

The construction of a reaction network containing all relevant intermediates and elementary reactions is necessary for the accurate description of chemical processes. In the case of a complex chemical reaction (involving, for instance, many reactants or highly reactive species), the size of such a network may grow rapidly. Here, we present a computational protocol that constructs such reaction networks in a fully automated fashion steered in an intuitive, graph-based fashion through a single graphical user interface. Starting from a set of initial reagents new intermediates are explored through intra- and intermolecular reactions of already explored intermediates or new reactants presented to the network. This is done by assembling reactive complexes based on heuristic rules derived from conceptual electronic-structure theory and exploring the corresponding approximate reaction path. A subsequent path refinement leads to a minimum-energy path which connects the new intermediate to the existing ones to form a connected reaction network. Tree traversal algorithms are then employed to detect reaction channels and catalytic cycles. We apply our protocol to the formose reaction to study different pathways of sugar formation and to rationalize its autocatalytic nature.

Entities:  

Year:  2017        PMID: 29084387     DOI: 10.1021/acs.jctc.7b00945

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  9 in total

1.  Knowledge discovery through chemical space networks: the case of organic electronics.

Authors:  Christian Kunkel; Christoph Schober; Harald Oberhofer; Karsten Reuter
Journal:  J Mol Model       Date:  2019-03-07       Impact factor: 1.810

2.  Environmental conditions drive self-organization of reaction pathways in a prebiotic reaction network.

Authors:  William E Robinson; Elena Daines; Peer van Duppen; Thijs de Jong; Wilhelm T S Huck
Journal:  Nat Chem       Date:  2022-06-06       Impact factor: 24.274

3.  A benchmark dataset for Hydrogen Combustion.

Authors:  Xingyi Guan; Akshaya Das; Christopher J Stein; Farnaz Heidar-Zadeh; Luke Bertels; Meili Liu; Mojtaba Haghighatlari; Jie Li; Oufan Zhang; Hongxia Hao; Itai Leven; Martin Head-Gordon; Teresa Head-Gordon
Journal:  Sci Data       Date:  2022-05-17       Impact factor: 8.501

Review 4.  The Matter Simulation (R)evolution.

Authors:  Alán Aspuru-Guzik; Roland Lindh; Markus Reiher
Journal:  ACS Cent Sci       Date:  2018-02-06       Impact factor: 14.553

5.  Efficient prediction of reaction paths through molecular graph and reaction network analysis.

Authors:  Yeonjoon Kim; Jin Woo Kim; Zeehyo Kim; Woo Youn Kim
Journal:  Chem Sci       Date:  2017-12-12       Impact factor: 9.825

Review 6.  A Trajectory-Based Method to Explore Reaction Mechanisms.

Authors:  Saulo A Vázquez; Xose L Otero; Emilio Martinez-Nunez
Journal:  Molecules       Date:  2018-11-30       Impact factor: 4.411

7.  Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis.

Authors:  Miguel Steiner; Markus Reiher
Journal:  Top Catal       Date:  2022-01-13       Impact factor: 2.910

8.  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

Review 9.  Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities.

Authors:  Idil Ismail; Raphael Chantreau Majerus; Scott Habershon
Journal:  J Phys Chem A       Date:  2022-10-03       Impact factor: 2.944

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.