Literature DB >> 31066549

Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling.

Seihwan Ahn1,2, Mannkyu Hong1,2, Mahesh Sundararajan1,2, Daniel H Ess3, Mu-Hyun Baik1,2.   

Abstract

Until recently, computational tools were mainly used to explain chemical reactions after experimental results were obtained. With the rapid development of software and hardware technologies to make computational modeling tools more reliable, they can now provide valuable insights and even become predictive. In this review, we highlighted several studies involving computational predictions of unexpected reactivities or providing mechanistic insights for organic and organometallic reactions that led to improved experimental results. Key to these successful applications is an integration between theory and experiment that allows for incorporation of empirical knowledge with precise computed values. Computer modeling of chemical reactions is already a standard tool that is being embraced by an ever increasing group of researchers, and it is clear that its utility in predictive reaction design will increase further in the near future.

Year:  2019        PMID: 31066549     DOI: 10.1021/acs.chemrev.9b00073

Source DB:  PubMed          Journal:  Chem Rev        ISSN: 0009-2665            Impact factor:   60.622


  11 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.  The (not so) simple prediction of enantioselectivity - a pipeline for high-fidelity computations.

Authors:  Rubén Laplaza; Jan-Grimo Sobez; Matthew D Wodrich; Markus Reiher; Clémence Corminboeuf
Journal:  Chem Sci       Date:  2022-05-18       Impact factor: 9.969

Review 3.  Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future.

Authors:  Andrew F Zahrt; Soumitra V Athavale; Scott E Denmark
Journal:  Chem Rev       Date:  2019-12-30       Impact factor: 60.622

Review 4.  Exploiting attractive non-covalent interactions for the enantioselective catalysis of reactions involving radical intermediates.

Authors:  Rupert S J Proctor; Avene C Colgan; Robert J Phipps
Journal:  Nat Chem       Date:  2020-10-22       Impact factor: 24.427

Review 5.  Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.

Authors:  Tânia F G G Cova; Alberto A C C Pais
Journal:  Front Chem       Date:  2019-11-26       Impact factor: 5.221

6.  Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex.

Authors:  Pascal Friederich; Gabriel Dos Passos Gomes; Riccardo De Bin; Alán Aspuru-Guzik; David Balcells
Journal:  Chem Sci       Date:  2020-04-07       Impact factor: 9.825

7.  Computer-assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands.

Authors:  Sibo Lin; Jenna C Fromer; Yagnaseni Ghosh; Brian Hanna; Mohamed Elanany; Wei Xu
Journal:  Sci Rep       Date:  2021-02-25       Impact factor: 4.379

8.  Discovery of a synthesis method for a difluoroglycine derivative based on a path generated by quantum chemical calculations.

Authors:  Tsuyoshi Mita; Yu Harabuchi; Satoshi Maeda
Journal:  Chem Sci       Date:  2020-05-22       Impact factor: 9.825

9.  Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization.

Authors:  Steven M Maley; Doo-Hyun Kwon; Nick Rollins; Johnathan C Stanley; Orson L Sydora; Steven M Bischof; Daniel H Ess
Journal:  Chem Sci       Date:  2020-08-21       Impact factor: 9.825

10.  Design Platform for Sustainable Catalysis with Radicals: Electrochemical Activation of Cp2 TiCl2 for Catalysis Unveiled.

Authors:  Tobias Hilche; Philip H Reinsberg; Sven Klare; Theresa Liedtke; Luise Schäfer; Andreas Gansäuer
Journal:  Chemistry       Date:  2021-01-12       Impact factor: 5.236

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