| Description |
1 online resource (xiv, 283 pages) : illustrations |
| Contents |
Cover; Contents; Preface; Acronyms; 1. Fitting Potential-Energy Hypersurfaces; 1.1. Introduction; 1.2. Empirical and Semi-Empirical Potential Surfaces; 1.3. Ab Initio Potential-Energy Surfaces (PESs); 1.4. Other Fitting Methods for Potential-Energy Surfaces; 1.5. Neural Network (NN) Approach; 1.6. Essential Steps in a Molecular Dynamics Simulations; 1.7. Organization of the Monograph; 2. Overview of Some Non-Neural Network Methods for Fitting Ab Initio Potential-Energy Databases; 2.1. Introduction; 2.2. Moving Shepard Interpolation (MSI) Methods; 2.2.1. Required Input Data |
| Summary |
This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Chemical reactions -- Data processing
|
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Neural networks (Computer science)
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SCIENCE -- Chemistry -- Physical & Theoretical.
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Chemical reactions -- Data processing
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Neural networks (Computer science)
|
| Form |
Electronic book
|
| Author |
Raff, Lionel M
|
| ISBN |
9780199909889 |
|
0199909881 |
|
9780197563113 |
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0197563112 |
|
