Interatomic Potential Functions of Sodium and Potassium

1956 ◽  
Vol 25 (4) ◽  
pp. 609-613 ◽  
Author(s):  
Rufus C. Ling
Keyword(s):  
Interatomic Potential ◽  
Potential Functions ◽  
Sodium And Potassium

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.

THE ROLE OF POTENTIAL FUNCTIONS IN THE MECHANICAL BEHAVIOR OF THE SINGLE WALL CARBON NANOTUBES

2012 ◽  
Vol 11 (03) ◽  
pp. 1240009 ◽  
Author(s):  
KEKA TALUKDAR ◽  
APURBA KRISHNA MITRA
Keyword(s):  
Mechanical Properties ◽  
Carbon Nanotubes ◽  
Tensile Strength ◽  
Interatomic Potential ◽  
Tight Binding ◽  
Flow Region ◽  
Potential Functions ◽  
Dynamics Simulation ◽  
Single Wall Carbon Nanotubes ◽  
Single Wall Carbon

Carbon nanotubes have been identified as the promising agents in reinforcing composite materials to achieve desired mechanical properties. In this study, three different types of single wall carbon nanotubes (SWCNTs) are subjected to molecular dynamics simulation to investigate their mechanical properties taking different interatomic potential functions. With unmodified Brenner's 2nd generation potential, a brittle fracture for all the SWCNTs is observed. But in tight-binding approach, the chiral and armchair SWCNTs exhibit somewhat extended plastic flow region before failure. With unmodified Brenner's potential, high tensile strength and ductility are observed for the armchair and chiral tubes. Y value of these two tubes is less than 1 TPa but more than 1 TPa for a zigzag tube. Much decrease of tensile strength and strain are noticed when we apply smoothing of the Brenner's potential at cut-off region. Failure stresses are dropped to much lower values for the three tubes. Ductility of the armchair and chiral tubes are also affected considerably by the choice of potential. Applying smoothing in the cut-off region to conserve the energy, the results show better agreement with the experimental findings.

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