Recent Work on Random Close Packing. 1. Polytetrahedral Nature of the Dense Disordered Packings of Hard Spheres PRL 98, 235504 (2007) A.V. Anikeenko and.

Slides:



Advertisements
Similar presentations
What Could We Do better? Alternative Statistical Methods Jim Crooks and Xingye Qiao.
Advertisements

On allocations that maximize fairness Uriel Feige Microsoft Research and Weizmann Institute.
Conics. Parabolas Definition - a parabola is the set of all points equal distance from a point (called the focus) and a line (called the directrix). Parabolas.
Counting Outcomes and Tree Diagrams
Alternative Simulation Core for Material Reliability Assessments Speculation how to heighten random character of probability calculations (concerning the.
Probability Distributions CSLU 2850.Lo1 Spring 2008 Cameron McInally Fordham University May contain work from the Creative Commons.
1 12. Principles of Parameter Estimation The purpose of this lecture is to illustrate the usefulness of the various concepts introduced and studied in.
Variance reduction techniques. 2 Introduction Simulation models should be coded such that they are efficient. Efficiency in terms of programming ensures.
Sampling Distributions
Lecture 4 The structure of crystalline solids L e a r n i n g O b j e c t i v es outcomes: 1.Describe the difference in atomic/molecular structure between.
Chapter 2 Motion Along a Straight Line In this chapter we will study kinematics, i.e., how objects move along a straight line. The following parameters.
INTEGRALS 5. INTEGRALS We saw in Section 5.1 that a limit of the form arises when we compute an area.  We also saw that it arises when we try to find.
Polymers PART.2 Soft Condensed Matter Physics Dept. Phys., Tunghai Univ. C. T. Shih.
Evaluating Hypotheses
2: Population genetics Break.
Advanced Topics in Data Mining Special focus: Social Networks.
Cluster Analysis (1).
On Distinguishing between Internet Power Law B Bu and Towsley Infocom 2002 Presented by.
Clustering with Bregman Divergences Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, Joydeep Ghosh Presented by Rohit Gupta CSci 8980: Machine Learning.
Copyright © Cengage Learning. All rights reserved. 5 Integrals.
Modeling Quality-Quantity based Communication Orr Srour under the supervision of Ishai Menache.
Properties, Handling and Mixing of Particulate Solids
States (Phases) of Matter
The Neymann-Pearson Lemma Suppose that the data x 1, …, x n has joint density function f(x 1, …, x n ;  ) where  is either  1 or  2. Let g(x 1, …,
Conics.
Decomposing Networks and Polya Urns with the Power of Choice Joint work with Christos Amanatidis, Richard Karp, Christos Papadimitriou, Martha Sideri Presented.
Statistical mechanics of random packings: from Kepler and Bernal to Edwards and Coniglio Hernan A. Makse Levich Institute and Physics Department City College.
Biological Networks Lectures 6-7 : February 02, 2010 Graph Algorithms Review Global Network Properties Local Network Properties 1.
Verification & Validation
Distinguishability of Hypotheses S.Bityukov (IHEP,Protvino; INR RAS, Moscow) N.Krasnikov (INR RAS, Moscow ) December 1, 2003 ACAT’2003 KEK, Japan S.Bityukov.
Stochastic sleep scheduling (SSS) for large scale wireless sensor networks Yaxiong Zhao Jie Wu Computer and Information Sciences Temple University.
1 Lesson 3: Choosing from distributions Theory: LLN and Central Limit Theorem Theory: LLN and Central Limit Theorem Choosing from distributions Choosing.
1 Statistical Distribution Fitting Dr. Jason Merrick.
Percent This slide needs the title “Percent”, your name, and two pictures that represent percent. Choose a nice background and apply it to all of your.
Chapter 5: Producing Data “An approximate answer to the right question is worth a good deal more than the exact answer to an approximate question.’ John.
1 Lesson 8: Basic Monte Carlo integration We begin the 2 nd phase of our course: Study of general mathematics of MC We begin the 2 nd phase of our course:
Random-Graph Theory The Erdos-Renyi model. G={P,E}, PNP 1,P 2,...,P N E In mathematical terms a network is represented by a graph. A graph is a pair of.
Crystal Structure A “unit cell” is a subdivision of the lattice that has all the geometric characteristics of the total crystal. The simplest choice of.
Conducting A Study Designing Sample Designing Experiments Simulating Experiments Designing Sample Designing Experiments Simulating Experiments.
1 M.Sc. Project of Hanif Bayat Movahed The Phase Transitions of Semiflexible Hard Sphere Chain Liquids Supervisor: Prof. Don Sullivan.
Random Sampling Approximations of E(X), p.m.f, and p.d.f.
PROBABILITY AND STATISTICS FOR ENGINEERING Hossein Sameti Department of Computer Engineering Sharif University of Technology Principles of Parameter Estimation.
Analyzing the Vulnerability of Superpeer Networks Against Attack Niloy Ganguly Department of Computer Science & Engineering Indian Institute of Technology,
March 23 & 28, Hashing. 2 What is Hashing? A Hash function is a function h(K) which transforms a key K into an address. Hashing is like indexing.
Chapter 7 Point Estimation of Parameters. Learning Objectives Explain the general concepts of estimating Explain important properties of point estimators.
Sampling and estimation Petter Mostad
Equilibrium statistical mechanics of network structures Physica A 334, 583 (2004); PRE 69, (2004); cond-mat ; cond-mat Thanks to:
Assignment for the course: “Introduction to Statistical Thermodynamics of Soft and Biological Matter” Dima Lukatsky In the first.
Algorithms For Solving History Sensitive Cascade in Diffusion Networks Research Proposal Georgi Smilyanov, Maksim Tsikhanovich Advisor Dr Yu Zhang Trinity.
INTEGRALS We saw in Section 5.1 that a limit of the form arises when we compute an area. We also saw that it arises when we try to find the distance traveled.
5 INTEGRALS.
Objectives Packing fraction in: Simple cubic unit cell
A Framework for Network Survivability Characterization Soung C. Liew and Kevin W. Lu IEEE Journal on Selected Areas in Communications, January 1994 (ICC,
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
Psy B07 Chapter 3Slide 1 THE NORMAL DISTRIBUTION.
Breakdown statistics in the large- electrode DC spark system Anders Korsbäck, BE-RF-LRF University of Helsinki Special thanks to Walter Wuensch and Jorge.
Kevin Stevenson AST 4762/5765. What is MCMC?  Random sampling algorithm  Estimates model parameters and their uncertainty  Only samples regions of.
Generating Random Variates
Kunimasa Miyazaki (CECAM meeting 06/28/2013) Hidden Amorphous Orders near the Jamming and Glass Transitions.
Lesson 8: Basic Monte Carlo integration
Questions about conditions and parameters
12. Principles of Parameter Estimation
Scientific Research Group in Egypt (SRGE)
From: Conduction in Jammed Systems of Tetrahedra
Break and Noise Variance
Statistical Process Control
Adjustment of Temperature Trends In Landstations After Homogenization ATTILAH Uriah Heat Unavoidably Remaining Inaccuracies After Homogenization Heedfully.
12. Principles of Parameter Estimation
Advanced Topics in Data Mining Special focus: Social Networks
Presentation transcript:

Recent Work on Random Close Packing

1. Polytetrahedral Nature of the Dense Disordered Packings of Hard Spheres PRL 98, (2007) A.V. Anikeenko and N. N. Medvedev 2. Is Random Close Packing of Spheres Well Defined? VOLUME 84, NUMBER 10 (2000) S. Torquato, T.M. Truskett, P. G. Debenedetti

The History J D Bernal raised the question in 1960 from observation; In the last 48 years, much research was done over this, but so far, few satisfactory conclusions have been reached

Why This Is An Important Question We can obtain a better insight of phase transition An EXPERT should be able to show you more its importance, but sorry, I am just a small potato

Definition (From Observation) The traditional experiments indicated an interesting limit One such experiment is done in 1969, limiting value was obtained simply from experiments. Nowadays, 2 ways of simulations are mainly applied

Can We Have A Theoretical Definition? Jammed system means all particles are “jammed” Introducing a parameter that quantifies “order” (difficult one, may be subjective) Maximally Random Jammed ( MRJ ) system is the one we desire, and its volume fraction is the RCP limit

Theoretical Definition By Introducing the Concept of Maximally random jammed A schematic plot of the order parameter versus volume fraction for a system of identical spheres

Independent of how we quantify order? Can we use simulations to have a feeling for the probability distribution for RCP to answer the question above?

How Can We Quantify Order In the paper Is Random Close Packing of Spheres Well Defined, they didn’t come up with an exact and simple way to quantify the order The paper Polytetrahedral Nature of the Dense Disordered Packings of Hard Spheres showed us an interesting phenomenon, which inspired me a simple way to quantify order

Quantify Order Using Polytetrahedral Nature In a tetrahedron, maximal edge length is e max, minimal edge length is 1 (i.e. the diameter of the sphere) Define a value Small values of unambiguously indicate that the shape of the simplex is close to regular tetrahedron with unit edges.

Define tetrahedra By simply trying, they found that the best value may be Here, we realize that the choose of the value may be subjective. But, as we go on, we saw phenomena independent of And, may just be a most obvious value

Volume fraction of tetrahedra

Fraction of spheres involved in tetrahedra

Polytetrahedral Aggregates Definition: clusters built from three or more face adjacent tetrahedra Isolated tetrahedra and pairs of tetrahedra (bipyramids) are omitted as they are found in the fcc and hcp crystalline structures.

Polytetrahedral Aggregates In the general case polytetrahedra have the form of branching chains and five-member rings combining in various ‘‘animals’’ MOTIF?

Volume fraction of polytetrahedra

Comparison Notice the difference between the 2 graphs

This gives me a hint for quantifying order I think, we should find the function for “volume fraction of isolated tetrahedra and bipyramids” against packing volume fraction, i.e. the difference between the 2 graph. We use L to denote it. From pure observation, we notice that L is approximately a constant at low density, but after 0.646, L has a sudden increase Quantify Order

We may use K to denote the volume fraction of spheres within polytetrahedral aggregates. To quantify disorder, we use K/L. Thus we use 1/(1+K/L) to denote order

Quantify Order But during the process, the value is chosen subjectively.

But from the left graph, we may expect that different choices of may lead to the same MRJ result. (This is only my naive expectation)

The Following Work May Be Done Obtain the - graph from simulation under different choices of Is the MRJ is independent of how we quantify order? Is the limit independent of Of course, the most important thing for me is to get into the problem

THANK YOU!