Neural Networks in Chemical Reaction Dynamics

Hardcover | January 3, 2012

byLionel Raff, Ranga Komanduri, MARTIN HAGAN

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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 andgradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structurecalculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions,NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level(Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabaticreactions.The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting,tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantummechanical studies in the years to come.

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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 configurat...

Lionel Raff is Regents Professor in the Department of Chemistry at Oklahoma State University. Ranga Komanduri is Professor and A. H. Nelson, Jr. Endowed Chair in Engineering in the School of Mechanical and Aerospace Engineering at Oklahoma State University. Martin Hagan is Professor in the School of Electrical and Computer Engineerin...
Format:HardcoverDimensions:336 pages, 0.12 × 0.12 × 0.12 inPublished:January 3, 2012Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199765650

ISBN - 13:9780199765652

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Table of Contents

1. Fitting Potential-Energy Hypersurfaces2. Overview of Some Non-NN Methods for Fitting Ab Initio Potential Energy Databases3. Feed-forward Neural Networks4. Configuration Space Sampling Methods5. Applications of NN Fitting of Potential-Energy Surfaces6. Potential Surfaces Using Expansion Methods and Neural Networks7. Genetic Algorithm (GA) and Internal Energy Transfer Calculations using NN Methods8. Empirical PES Fitting Using Feed-forward Neural Networks9. NN Methods for Data Analysis and Statistical Error Reduction10. Other Applications of NNs to Quantum Mechanical Problems11. Summary, Conclusions, and Future Trends