Literature DB >> 32105566

The Exploration of Chemical Reaction Networks.

Jan P Unsleber1, Markus Reiher1.   

Abstract

Modern computational chemistry has reached a stage at which massive exploration into chemical reaction space with unprecedented resolution with respect to the number of potentially relevant molecular structures has become possible. Various algorithmic advances have shown that such structural screenings must and can be automated and routinely carried out. This will replace the standard approach of manually studying a selected and restricted number of molecular structures for a chemical mechanism. The complexity of the task has led to many different approaches. However, all of them address the same general target, namely to produce a complete atomistic picture of the kinetics of a chemical process. It is the purpose of this overview to categorize the problems that should be targeted and to identify the principle components and challenges of automated exploration machines so that the various existing approaches and future developments can be compared based on well-defined conceptual principles. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 71 is April 20, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Year:  2020        PMID: 32105566     DOI: 10.1146/annurev-physchem-071119-040123

Source DB:  PubMed          Journal:  Annu Rev Phys Chem        ISSN: 0066-426X            Impact factor:   12.703


  8 in total

1.  Network Analysis Reveals Spatial Clustering and Annotation of Complex Chemical Spaces: Application to Astrochemistry.

Authors:  Alexander Ruf; Grégoire Danger
Journal:  Anal Chem       Date:  2022-10-09       Impact factor: 8.008

2.  O-Acetyl Migration within the Sialic Acid Side Chain: A Mechanistic Study Using the Ab Initio Nanoreactor.

Authors:  Lisa Oh; Yang Ji; Wanqing Li; Ajit Varki; Xi Chen; Lee-Ping Wang
Journal:  Biochemistry       Date:  2022-09-02       Impact factor: 3.321

3.  The transferability limits of static benchmarks.

Authors:  Thomas Weymuth; Markus Reiher
Journal:  Phys Chem Chem Phys       Date:  2022-06-22       Impact factor: 3.945

Review 4.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

5.  Towards Predictive Synthesis of Inorganic Materials Using Network Science.

Authors:  Alex Aziz; Javier Carrasco
Journal:  Front Chem       Date:  2021-12-21       Impact factor: 5.221

6.  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

7.  Deep reaction network exploration at a heterogeneous catalytic interface.

Authors:  Qiyuan Zhao; Yinan Xu; Jeffrey Greeley; Brett M Savoie
Journal:  Nat Commun       Date:  2022-08-18       Impact factor: 17.694

Review 8.  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

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.