Lionel Raff

author and co-author of Neural Networks in Chemical Reaction Dynamics

February 4, 2012

Dr. Lionel M. Raff is a Regents Professor at Oklahoma State University, a rank he has held for 33 years. He received his B.S and M.S. degrees from the University of Oklahoma graduating with Special Distinction and his Ph.D. from the University of Illinois while working with Professor Aaron Kuppermann in the area of electron impact spectroscopy. Professor Raff was a National Science Foundation Postdoctoral Research Fellow at Columbia University, New York City, where he worked in the area of molecular dynamics with Professor Martin Karplus. In addition to holding a faculty position at Oklahoma State University, he was a Visiting Scientist at the Los Alamos Scientific Laboratories in 1973. Dr. Raff has authored or co-authored over 200 articles primarily in the Journal of Chemical Physics, the Journal of Physical Chemistry, and Physical Review in the areas of gas-phase molecular dynamics, Monte Carlo methods, gas-surface interactions, gas-phase quantum scattering, and ab initio molecular dynamics. He is the author or co-author of six book chapters and three books including the comprehensive textbook "Principles of Physical Chemistry" (Prentice Hall, Upper Saddle River, NJ, 2001). He has been elected to Outstanding Educators of American. In 1979, he received the Oklahoma Chemist Award and the following year the "Oklahoma Scientist Award" from the Oklahoma Academy of Sciences. In 1993, he received the Oklahoma Medallion for Excellence in College/University Teaching from the Oklahoma Foundation for Excellence. Forty-four students have received advanced degrees working under his direction.

Neural Networks in Chemical Reaction Dynamic 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 initio 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 treatise has been 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 neural networks in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.

URL: https://library.okstate.edu/news/celebratingbooks/2012-honorees/lionel-raff

Last Updated: 12 January 2022