1. Learning proton NMR spectroscopy with computers
- Author
-
Michel Rouillard, Daniel Cabrol-Bass, and Jean-Pierre Rabine
- Subjects
Basis (linear algebra) ,business.industry ,Theoretical models ,Physics::Physics Education ,Machine learning ,computer.software_genre ,Proton nmr spectroscopy ,Nuclear magnetic resonance ,Simple (abstract algebra) ,Inverse operation ,Artificial intelligence ,Heuristics ,business ,computer - Abstract
Teaching chemistry students to deduce chemical structures from the results of spectroscopic methods suffers severe practical problems. Often the theoretical basis of the method lies beyond the student's comprehension. Although theoretical models allow the prediction of spectra of simple compounds from their structures, the models have limited utility for the inverse operation of determining structures from spectra. This later operation is primarily empirical and does not lend itself to a systematic teaching approach. The ability to elucidate chemical structures is built largely on implicit heuristics and informal experience accumulated by solving numerous examples.
- Published
- 1996
- Full Text
- View/download PDF