Neural Networks in Chemical Reaction Dynamics

1. Auflage 2012
ISBN: 978-0-19-976565-2
Verlag: Oxford University Press

Buch, Englisch, 336 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 617 g

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 potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations 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-adiabatic reactions.

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 quantum mechanical studies in the years to come.

Zielgruppe

Scientists/engineers working in the field of chemical reaction dynamics, graduate students in physical chemistry or chemical physics, scholars working on applications of neural networks, and individuals interested in biochemical applications

Autoren/Hrsg.

Raff, Lionel

Komanduri, Ranga

Hagan, Martin

Bukkapatnam, Satish

Fachgebiete

• Naturwissenschaften Physik Angewandte Physik Chemische Physik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Reaktionstechnik (incl. Katalyse, Elektrolyse)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Naturwissenschaften Chemie Physikalische Chemie

Inhaltsverzeichnis

Preface
Acronyms
Chapter 1: Fitting Potential-Energy Hypersurfaces
Chapter 2: Overview of Some Non-NN Methods for Fitting Ab Initio Potential Energy Databases
Chapter 3: Feed-forward Neural Networks
Chapter 4: Configuration Space Sampling Methods
Chapter 5: Applications of NN Fitting of Potential-Energy Surfaces
Chapter 6: Potential Surfaces Using Expansion Methods and Neural Networks
Chapter 7: Genetic Algorithm (GA) and Internal Energy Transfer Calculations using NN Methods
Chapter 8: Empirical PES Fitting Using Feed-forward Neural Networks
Chapter 9: NN Methods for Data Analysis and Statistical Error Reduction
Chapter 10: Other Applications of NNs to Quantum Mechanical Problems
Chapter 11: Summary, Conclusions, and Future Trends
References
Subject Index

Über Autor(innen)

Lionel Raff is Regents Professor in the Department of Chemistry at Oklahoma State University.

Ranga Komanduri is Professor & 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 Engineering, Oklahoma State University

Satish Bukkapatnam is Assistant Professor in the School of Industrial Engineering and Management at Oklahoma State University.