Physical Sciences and Mathematics Commons^™

Computational Neural Networks In Chemistry: Model Free Mapping Devices For Predicting Chemical Reactivity From Molecular Structure, David Wayne Elrod

Dissertations

Computational neural networks (CNNs) are a computational paradigm inspired by the brain’s massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs.

Chemical reactivity is here considered an example of …

Palladium(Ii) Catalysis, Glenn Noronha

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No abstract provided.

Low Temperature Spectroscopic And Annealing Studies Of Disordered Organic Solids, Dwayne L. Labrake

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No abstract provided.

Spin Label Studies Of Radical Reduction In Normal And Sickle Erythrocyte Membranes Associated With Antioxidants, Yin Zhang

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No abstract provided.

Lithium Transport, Mg2+ Competition, And Phospholipid Composition In Human Erythrocytes: A Multinuclear Magnetic Resonance Study, Aida Abraha

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No abstract provided.

Multinuclear Magnetic Resonance Study Of Chlorine And Cesium Ion Distribution In Human Erythrocyte Suspensions, Lisa Wittenkeller

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No abstract provided.

Syntheses Of Azure-B Neuropeptide Conjugates And A Novel Bicyclic Triene, Milind D. Choubal

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No abstract provided.

Study Of The Hexamethylenetetramine, Ammonia, And Formaldehyde System: Quantitative Determinations, Gary Lee Madsen

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No abstract provided.

Remote Dianions In The Synthesis Of Indolizidine Alkaloids, Diana L. C. Green Green

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No abstract provided.