Learning Techniques in Chemistry

The following sections present a more detailed description of the methods mentioned above. An overview of machine learning techniques in chemistry is given in Chapter IX, Section 1 in the Handbook. 

Rose, J.R. (2003) Machine learning techniques in chemistry, in Handbook of Chemoinformatics, Vol. 3 (ed. J. Gasteiger), Wiley-VCH Verlag GmbH, Weinheim, Germany, pp. 1082-1097. 

Concept Learning (see Machine Learning Techniques in Chemistry)... 

Artificial Intelligence in Chemistry Machine Learning Techniques in Chemistry Neural Networks in Chemistry. 

Chemometrics provides methods for reducing the huge amounts of data which can nowadays be produced easily by automated instruments and finding out the driving factors of a chemical system when theoretical concepts alone are not successful (see Artificial Intelligence in Chemistry Experimental Design and Machine Learning Techniques in Chemistry). 

Algorithms Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry Molecular Models Visualization Neural Networks in Chemistry NMR Data Correlation with Chemical Structure Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Shape Analysis Spectroscopic Databases Spectroscopy Computational Methods Structure Determination by Computer-based Spectrum Interpretation Zeolites Applications of Computational Methods. 

Conformational Flexibility in 3D Structure Searching Machine Learning Techniques in Chemistry Molecular Dynamics Simulations of Nucleic Acids Nucleic Acid Conformation and Flexibility Modeling Using Molecular... 

What this adds up to is simply the fact that your study of organic chemistry must integrate the general with the specific. You must not only learn general patterns but also how to apply them to specific molecules, and you must also learn the behavior of specific molecules in order to see where patterns come from. These skills can be learned in a variety of ways, but one of the most effective learning techniques is to study models of molecules that duplicate their size, shape, stability, and other chemically important properties. That is where this workbook comes in. 

Following upon the success of the ACOL series, which by its very name is predominately concerned with Analytical Chemistry, the Analytical Techniques in the Sciences (AnTs) series of open learning texts has now been introduced with the aim of providing a broader coverage of the many areas of science in which analytical techniques and methods are now increasingly applied. With this in mind, the AnTs series of texts seeks to provide a range of books which will cover not only the actual techniques themselves, but also those scientific disciplines which have a necessary requirement for analytical characterization methods. 

Despite all the information that might be obtained using Mossbauer spectroscopy, some of its limitations naturally discouraged many chemists from using this new technique. Unfamiliarity with the basic principles, the fact that most of the early work was done only on iron and tin, and the lack of commercially available research quality equipment until 1965 were other reasons for the lack of interest. This symposium. The Mossbauer Effect and Its Application in Chemistry, was sponsored by Nuclear Science (formerly Nuclear Science Engineering Corp.), a division of International Chemical Nuclear Corp., with the hope that more chemists would learn how Mossbauer spectroscopy has been and can be used. 

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