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Books > Science & Mathematics > Chemistry > Physical chemistry > Quantum & theoretical chemistry
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Neural Networks in Chemical Reaction Dynamics (Hardcover)
Loot Price: R2,866
Discovery Miles 28 660
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Neural Networks in Chemical Reaction Dynamics (Hardcover)
Expected to ship within 12 - 19 working days
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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 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.
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