An approximate estimation of the effect of secondary interaction on BGY pair potential

1983 ◽  
Vol 264O (1) ◽  
Author(s):  
T. K. Chatterjee ◽  
A. Guha
Keyword(s):  
Pair Potential ◽  
Secondary Interaction ◽  
Approximate Estimation

An ab initio pair potential for Ne2 and the equilibrium properties of neon

1998 ◽  
Vol 95 (3) ◽  
pp. 471-475 ◽  
Author(s):  
GREG GROCHOLA SALVY RUSSO IAN SNOOK
Keyword(s):  
Ab Initio ◽  
Pair Potential

Interionic Pair Potential, hard sphere diameter and entropy of mixing of NaCd compound forming binary molten alloys under the framework of Pseudopotential theory

2014 ◽  
Vol 10 (6) ◽  
pp. 2843-2852
Author(s):  
Sujeet Kumar Chatterjee ◽  
Lokesh Chandra Prasad ◽  
Ajaya Bhattarai
Keyword(s):  
Nearest Neighbor ◽  
Effective Interaction ◽  
Pair Potential ◽  
Sphere Diameter ◽  
Compound Formation ◽  
Pseudopotential Theory ◽  
Energy Of Mixing ◽  
Asymmetric Behaviour ◽  
Statistical Mechanical ◽  
Free Energy Of Mixing

The observed asymmetric behaviour of mixing of  NaCd liquid alloys around equiatomic composition with smaller negative values for free energy of mixing at compound forming concentration, i.e. GMXS = -4.9KJ at Ccd =0.66 has  aroused our interest to undertake a theoretical investigation of this system.A simple statistical mechanical theory based on compound formation model has been used to investigate the energetics of formation of intermetallic compound Cd2Na in the melt through the study of entropy of mixing.Besides, the interionic interactions between component atoms Na and Cd of the alloys have been understood through the study of interionic pair potential фij(r), calculated from pseudopotential theory in the light of CF model.Our study of фij(r) suggest that the effective interaction between Na-Na atoms decreases on alloying with Cd atom, being minimum for compound forming alloy( Cd 0.66 Na 0.34 ).The nearest neighbor distance between Na-Na atoms does not alter on alloying. Like wise Na-Na,  effective interaction between  Cd-Cd atom decreases from pure state to NaCd alloys, being smaller at compound forming  concentration Cd 0.66 Na 0.34.The computed values of SM from pseudopotential theory are positive at all concentrations, but the agreement between theory and experimental is not satisfactory. This might be happening due to parameterisation of σ3 and Ψcompound.

Random Forest Refinement of Pairwise Potentials for Protein-ligand Decoy Detection

2019 ◽  
Author(s):  
Jun Pei ◽  
Zheng Zheng ◽  
Hyunji Kim ◽  
Lin Song ◽  
Sarah Walworth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Probability Function ◽  
Pair Potential ◽  
Scoring Function ◽  
Stable Structure ◽  
Scoring Functions ◽  
Atom Pair ◽  
Data Set ◽  
Atom Pairs

An accurate scoring function is expected to correctly select the most stable structure from a set of pose candidates. One can hypothesize that a scoring function’s ability to identify the most stable structure might be improved by emphasizing the most relevant atom pairwise interactions. However, it is hard to evaluate the relevant importance for each atom pair using traditional means. With the introduction of machine learning methods, it has become possible to determine the relative importance for each atom pair present in a scoring function. In this work, we use the Random Forest (RF) method to refine a pair potential developed by our laboratory (GARF6) by identifying relevant atom pairs that optimize the performance of the potential on our given task. Our goal is to construct a machine learning (ML) model that can accurately differentiate the native ligand binding pose from candidate poses using a potential refined by RF optimization. We successfully constructed RF models on an unbalanced data set with the ‘comparison’ concept and, the resultant RF models were tested on CASF-2013.5 In a comparison of the performance of our RF models against 29 scoring functions, we found our models outperformed the other scoring functions in predicting the native pose. In addition, we used two artificial designed potential models to address the importance of the GARF potential in the RF models: (1) a scrambled probability function set, which was obtained by mixing up atom pairs and probability functions in GARF, and (2) a uniform probability function set, which share the same peak positions with GARF but have fixed peak heights. The results of accuracy comparison from RF models based on the scrambled, uniform, and original GARF potential clearly showed that the peak positions in the GARF potential are important while the well depths are not. <br>

Tribology of rail bogie

2018 ◽  
Vol 77 (4) ◽  
pp. 230-240
Author(s):  
D. P. Markov
Keyword(s):  
Contact Fatigue ◽  
Basic Element ◽  
Difficult Problem ◽  
Railway Track ◽  
Rolling Stock ◽  
Kinematic Parameters ◽  
Approximate Estimation ◽  
Railway Bogie ◽  
Classical Calculation

Railway bogie is the basic element that determines the force, kinematic, power and other parameters of the rolling stock, and its movement in the railway track has not been studied enough. Classical calculation of the kinematic and dynamic parameters of the bogie's motion with the determination of the position of its center of rotation, the instantaneous axes of rotation of wheelsets, the magnitudes and directions of all forces present a difficult problem even in quasi-static theory. The paper shows a simplified method that allows one to explain, within the limits of one article, the main kinematic and force parameters of the bogie movement (installation angles, clearance between the wheel flanges and side surfaces of the rails), wear and contact damage to the wheels and rails. Tribology of the railway bogie is an important part of transport tribology, the foundation of the theory of wheel-rail tribosystem, without which it is impossible to understand the mechanisms of catastrophic wear, derailments, contact fatigue, cohesion of wheels and rails. In the article basic questions are considered, without which it is impossible to analyze the movement of the bogie: physical foundations of wheel movement along the rail, types of relative motion of contacting bodies, tribological characteristics linking the force and kinematic parameters of the bogie. Kinematics and dynamics of a two-wheeled bogie-rail bicycle are analyzed instead of a single wheel and a wheelset, which makes it clearer and easier to explain how and what forces act on the bogie and how they affect on its position in the rail track. To calculate the motion parameters of a four-wheeled bogie, it is represented as two two-wheeled, moving each on its own rail. Connections between them are replaced by moments with respect to the point of contact between the flange of the guide wheel and the rail. This approach made it possible to give an approximate estimation of the main kinematic and force parameters of the motion of an ideal bogie (without axes skewing) in curves, to understand how the corners of the bogie installation and the gaps between the flanges of the wheels and rails vary when moving with different speeds, how wear and contact injuries arise and to give recommendations for their assessment and elimination.

Effect of changes in pair potential on the dynamical structure factor of molecular crystals

1980 ◽  
Vol 40 (6) ◽  
pp. 1517-1521 ◽  
Author(s):  
C.S. Murthy ◽  
K. Singer ◽  
M.L. Klein ◽  
I.R. McDonald
Keyword(s):  
Structure Factor ◽  
Molecular Crystals ◽  
Pair Potential ◽  
Dynamical Structure ◽  
Dynamical Structure Factor

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

Sign in / Sign up

Export Citation Format

Share Document