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Neural networks in chemical reaction dynamics / Lionel M. Raff ... [et al.]

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: New York : Oxford University Press, c2012Edition: Online-AusgDescription: Online-Ressource (1 online resource (xiv, 283 p.)) : illISBN:
  • 9781280594816
  • 1280594810
  • 9780199909889
Subject(s): Additional physical formats: 0199765650 | 9780199765652. | 1280591447 | Erscheint auch als: Neural networks in chemical reaction dynamics. Druck-Ausgabe. Oxford [u.a.] : Oxford Univ. Press, 2012. XIV, 283 S.DDC classification:
  • 541/.390285632 22
  • 541.390285632
RVK: RVK: VC 6320LOC classification:
  • QD501
Online resources: Summary: This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics.Summary: 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 -- 2.2.2. MSI Method for Molecules with Four or Fewer Atoms -- 2.2.3. MSI Method for Molecules with More than Four Atoms -- 2.2.4. MSI Configuration Space Sampling -- 2.2.5. Applications and Results -- 2.3. Interpolative Moving Least-Squares Methods (IMLS) -- 2.3.1. General Method -- 2.3.2. Cutoff Function, Basis Sets, and Data Sampling -- 2.3.3. Applications and Results -- 2.4. Invariant Polynomial (IP) and Reproducing Kernel Hilbert Space (RKHS) Methods -- 2.4.1. Invariant Polynomial Methods -- 2.4.2. Applications and Results of IP Methods -- 2.4.3. Reproducing Kernal Hilbert Space (RKHS) -- 2.5. Hybrid Methods -- 2.5.1. Application to H[sub(3)] System -- 2.5.2. Application to the O([sup(1)]D) + H[sub(2)] System -- 2.6. Neural Networks Applications to Reaction Dynamics -- 3. Feedforward Neural Networks -- 3.1. Introduction -- 3.2. Neuron Model -- 3.3. Network Architectures -- 3.4. Approximation Capabilities of Multilayer Networks -- 3.5. Training Multilayer Networks -- 3.6. Generalization (Interpolation and Extrapolation) -- 3.7. Data Preprocessing -- 3.8. Practical Aspects of NN Training Issues -- 3.8.1. Database, Local Minima, Sampling Bias, Committees, and Derivatives -- 3.8.2. Input Vector Optimization and Fitting Accuracy.PPN: PPN: 807356727Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE
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