Summary, Conclusions, and Future Trends Chapter uri icon

abstract

  • Since the introduction of classical and semiclassical molecular dynamics (MD) methods in the 1960s and Gaussian procedures to conduct electronic structure calculations in the 1970s, a principal objective of theoretical chemistry has been to combine the two methods so that MD and quantum mechanical studies can be conducted on ab initio potential surfaces. Although numerous procedures have been attempted, the goal of first principles, ab initio dynamics calculations has proven to be elusive when the system contains five or more atoms moving in unrestricted three-dimensional space. For many years, the conventional wisdom has been that ab initio MD calculations for complex systems containing five or more atoms with several open reaction channels are presently beyond our computational capabilities. The rationale for this view are (a) the inherent difficulty of high level ab initio quantum calculations on complex systems that may take numerous, large-scale computations impossible, (b) the large dimensionality of the configuration space for such systems that makes it necessary to examine prohibitively large numbers of nuclear configurations, and (c) the extreme difficulty associated with obtaining sufficiently converged results to permit accurate interpolation of numerical data obtained from electronic structure calculations when the dimensionality of the system is nine or greater. Neural networks (NN) derive their name from the fact that their interlocking structure superficially resembles the neural network of a human brain and from the fact that NNs can sense the underlying correlations that exist in a database and properly map them in a manner analogous to the way a human brain can execute pattern recognition. Artificial neurons were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, an MIT logician. NNs have been employed by engineers for decades to assist in the solution of a multitude of problems. Nevertheless, the power of NNs to assist in the solution of numerous problems that occur in chemical reaction dynamics is just now being realized by the chemistry community.

author list (cited authors)

  • Raff, L., Komanduri, R., Hagan, M., & Bukkapatnam, S.

citation count

  • 0

Book Title

  • Neural Networks in Chemical Reaction Dynamics

publication date

  • February 2012