{"product_id":"neural-networks-in-chemical-reaction-dynamics-9780199765652","title":"Neural Networks in Chemical Reaction Dynamics","description":"\u003cp\u003e • Author(s): Lionel Raff | Ranga Komanduri | Martin Hagan\u003cbr\u003e • Publisher: Oxford University Press\u003cbr\u003e • Publisher Imprint: Oxford University Press\u003cbr\u003e • BISAC: Chemistry - Physical \u0026amp; Theoretical\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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; sc\u003c\/p\u003e","brand":"Oxford University Press","offers":[{"title":"Hardcover","offer_id":47599063105687,"sku":"9780199765652","price":16419.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780199765652.webp?v=1775002992","url":"https:\/\/atlanticbooks.com\/products\/neural-networks-in-chemical-reaction-dynamics-9780199765652","provider":"Atlantic Books","version":"1.0","type":"link"}