Evaluation of the interatomic potential functions for rare gases with the use of the reduced potential curve method

1964 ◽  
Vol 29 (12) ◽  
pp. 2881-2891 ◽  
Author(s):  
F. Jenč
Keyword(s):  
Interatomic Potential ◽  
Potential Functions ◽  
Potential Curve ◽  
Rare Gases ◽  
Curve Method

THE ZERO-POINT KINETIC ENERGY OF LIQUID HELIUM

1955 ◽  
Vol 33 (12) ◽  
pp. 797-800 ◽  
Author(s):  
D. G. Henshaw ◽  
D. G. Hurst
Keyword(s):  
Kinetic Energy ◽  
Neutron Diffraction ◽  
Liquid Helium ◽  
Latent Heat ◽  
Interatomic Potential ◽  
Zero Point ◽  
Potential Functions ◽  
Point Temperature ◽  
Heat Of Vaporization ◽  
Latent Heat Of Vaporization

The zero-point kinetic energy of liquid helium has been calculated from the interatomic potential, the latent heat of vaporization, and atomic distributions derived from neutron diffraction measurements. Calculations were carried out for two liquid temperatures and several published interatomic potential functions. The resulting values of the "zero-point temperature" lie between 9.0°K. and 12.6°K.

The Use of Self

2011 ◽  
Author(s):  
Raymond Fox
Keyword(s):  
Neural Networks ◽  
Electronic Structure ◽  
Chemical Reaction ◽  
Scaling Laws ◽  
Interatomic Potential ◽  
Statistical Error ◽  
Reaction Dynamics ◽  
Potential Functions ◽  
Phase Reaction ◽  
Chemical Reaction Dynamics

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.

Neural Networks in Chemical Reaction Dynamics

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Neural Networks ◽  
Electronic Structure ◽  
Chemical Reaction ◽  
Scaling Laws ◽  
Interatomic Potential ◽  
Statistical Error ◽  
Reaction Dynamics ◽  
Potential Functions ◽  
Phase Reaction ◽  
Chemical Reaction Dynamics

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.

Reduced double-minimum potential curves for XY3 pyramidal molecules

1985 ◽  
Vol 50 (7) ◽  
pp. 1519-1536 ◽  
Author(s):  
Vladimír Špirko ◽  
Svatopluk Civiš ◽  
Stanislav Beran ◽  
Petr Čársky ◽  
Jürgen Fabian
Keyword(s):  
Experimental Data ◽  
Ab Initio ◽  
Degree Of Approximation ◽  
Potential Functions ◽  
Potential Curve ◽  
Wide Range ◽  
Minimum Potential ◽  
High Degree ◽  
Range Of Values ◽  
Potential Curves

The reduced potential curve (RPC) method used by Jenc and Pliva for studying the diatomic potentials is adapted for three-parameter studies of the inversional double-minimum potential functions of XY3 pyramidal molecules. Reduced double-minimum potential curves (RDMPC's) of the first, second and third row hydrides (CH3-, NH3, OH3+; SiH3-, PH3, SH3+; GeH3-, AsH3, SeH3+) are constructed using CNDO/2 and ab initio MBPT(2) theoretical potentials. The theoretical RDMPC's corresponding to a group of isoelectronic hydrides coincide to a high degree of approximation, so that they can be represented by a single curve. Furthermore, there is a nearly perfect coincidence between the theoretical RDMPC's of the first row hydrides and the ammonia experimental RDMPC (the only curve known experimentally). To illustrate a practical use of the proposed RPC approach, several approximants to the genuine phosphine potential are constructed (over a wide range of values for the inversion motion coordinate) by combining the available experimental data and the calculated RDMPC's. The resulting potentials exhibit a very close coincidence.

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