Literature DB >> 20419205

Heuristic thinking makes a chemist smart.

Nicole Graulich1, Henning Hopf, Peter R Schreiner.   

Abstract

We focus on the virtually neglected use of heuristic principles in understanding and teaching of organic chemistry. As human thinking is not comparable to computer systems employing factual knowledge and algorithms--people rarely make decisions through careful considerations of every possible event and its probability, risks or usefulness--research in science and teaching must include psychological aspects of the human decision making processes. Intuitive analogical and associative reasoning and the ability to categorize unexpected findings typically demonstrated by experienced chemists should be made accessible to young learners through heuristic concepts. The psychology of cognition defines heuristics as strategies that guide human problem-solving and deciding procedures, for example with patterns, analogies, or prototypes. Since research in the field of artificial intelligence and current studies in the psychology of cognition have provided evidence for the usefulness of heuristics in discovery, the status of heuristics has grown into something useful and teachable. In this tutorial review, we present a heuristic analysis of a familiar fundamental process in organic chemistry--the cyclic six-electron case, and we show that this approach leads to a more conceptual insight in understanding, as well as in teaching and learning.

Entities:  

Year:  2009        PMID: 20419205     DOI: 10.1039/b911536f

Source DB:  PubMed          Journal:  Chem Soc Rev        ISSN: 0306-0012            Impact factor:   54.564


  5 in total

1.  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

2.  Predicting Absolute Rate Constants for Huisgen Reactions of Unsaturated Iminium Ions with Diazoalkanes.

Authors:  Jingjing Zhang; Quan Chen; Robert J Mayer; Jin-Dong Yang; Armin R Ofial; Jin-Pei Cheng; Herbert Mayr
Journal:  Angew Chem Int Ed Engl       Date:  2020-05-11       Impact factor: 15.336

3.  Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments.

Authors:  Vasilios Duros; Jonathan Grizou; Abhishek Sharma; S Hessam M Mehr; Andrius Bubliauskas; Przemysław Frei; Haralampos N Miras; Leroy Cronin
Journal:  J Chem Inf Model       Date:  2019-05-22       Impact factor: 4.956

4.  Controlling an organic synthesis robot with machine learning to search for new reactivity.

Authors:  Jarosław M Granda; Liva Donina; Vincenza Dragone; De-Liang Long; Leroy Cronin
Journal:  Nature       Date:  2018-07-18       Impact factor: 49.962

5.  Effective in silico prediction of new oxazolidinone antibiotics: force field simulations of the antibiotic-ribosome complex supervised by experiment and electronic structure methods.

Authors:  Jörg Grunenberg; Giuseppe Licari
Journal:  Beilstein J Org Chem       Date:  2016-03-04       Impact factor: 2.883

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.