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Neural networks in chemical reaction dynamics / Lionel M. Raff ... [et al.]
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Oxford ; New York : Oxford University Press, c2012Auflage: Online-AusgBeschreibung: Online-Ressource (1 online resource (xiv, 283 p.)) : illISBN:- 9781280594816
- 1280594810
- 9780199909889
- 9780199765652
- 541.390285632
- 541/.390285632 22
- QD501
Inhalte:
Zusammenfassung: This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics.Zusammenfassung: 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 | ZDB-38-EBR
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 Atoms2.2.3. 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)] System2.5.2. 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
3.9. Example Training Process (MATLAB)3.10. The Combined Function Derivative Approximation (CFDA) NN Method; 3.11. Combined Function Derivative Approximation Pruning; 3.11.1. Two-Layer Network Response; 3.11.2. Two-Layer Network Response; 3.11.3. Pruning Algorithm Summary; 4. Configuration Space Sampling Methods; 4.1. Introduction; 4.2. Trajectory and Novelty Sampling (NS) Methods; 4.3. Self-Starting Method Using Direct Dynamics (DD); 4.4. Configuration Sampling Using a Gradient Fitting Method; 5. Applications of Neural Network Fitting of Potential-Energy Surfaces; 5.1. Introduction
5.2. Near Equilibrium Structures-Vibrational State Studies5.2.1. The H[omitted] Molecular Ion; 5.2.2. H[sub(2)]O, HOOH, and H[sub(2)]CO; 5.3. CFDA Fitting-The H + H'Br ? HBr + H' and H[sub(2)] + Br Reactions; 5.4. cis-trans Isomerization and N-O Dissociation Reactions of HONO; 5.5. Gradient Sampling-Unimolecular Dissociation of HOOH to 2 OH; 5.6. Unimolecular Dissociation of Vinyl Bromide (H[sub(2)]C = CHBr); 5.7. Non-Adiabatic Reactions: SiO[sub(2)] ? SiO + O and SiO[sub(2)] ? Si + O[sub(2)]; 5.8. Generalized NN Representation of High-Dimensional Potential-Energy Surfaces
6. Potential-Energy Surfaces Using Expansion Methods and Neural Networks
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