Start with the protocol: where the states come from
Section II A and Eq. (1) define the model: two particles repel only when they overlap, with energy . In 2D they use a 50–50 mixture of disks with size ratio 1.4, which prevents crystallisation, and particle numbers from 4 to 4096.
States start at infinite temperature: centres placed completely at random in a periodic box, then conjugate gradients walk downhill in energy. Minimisation stops when the energy per particle falls below or changes by less than between iterations. The first condition is the test for “unjammed”, equivalent to a pressure below .
Keep in mind that “jammed” in this paper is a statement about this protocol. Another way of making states, such as slow cooling, gives other thresholds. Every distribution and extrapolation later in the paper is a statistic over these random starts.
Source: C. S. O’Hern
How one state’s threshold is measured (Section II B)
For each zero-temperature state: if the pressure is zero, every particle is grown by the same factor; if not, every particle is shrunk; after each step the energy is minimised again, until the pressure just becomes non-zero. Steps range from to , smaller near the threshold, and are refined until the final configuration no longer depends on the step.
The key finding of this section is that the bulk and shear moduli vanish at the same packing fraction. Resisting compression and resisting shear begin together, so each state has a well-defined threshold. The authors also checked the dynamical matrix: at least 1e-6 above the threshold, the only zero-frequency modes belong to rattlers and to uniform translations.
Watch out at Fig. 3. In the arXiv v1 we read, the caption gives reference slopes of 2.0 and 2.5, while Table I gives pressure exponents of 1.01 and 1.50, the same values Chapter 4 derived from . The values 2.0 and 2.5 are the energy exponent . We checked pressure against Table I; the caption of the published version is one we have not seen.
Source: C. S. O’Hern
From the jammed fraction to the threshold distribution (Figs. 5 to 8)
Fig. 5 shows , the fraction of random starts already jammed at a given packing fraction: a step rising from 0 to 1. Fig. 6 is its derivative, the threshold distribution . The distributions for three contact exponents lie on top of each other within error: the threshold is set by geometry alone, not by how stiff the contacts are.
Fig. 7 plots the full width at half maximum against particle number. Read it in two parts: for very few particles the width first grows with size, and only past its maximum does it fall as Eq. (3). The values and apply to the second part only.
Fig. 8 deals with the position of the peak, . It approaches a limit as a power of the system length , Eq. (4), with fitted and . So is an extrapolation, not something any single simulation measured.
Source: C. S. O’Hern
Contacts and Table I: which numbers rest on an assumption
Section II E fits Fig. 9 with Eq. (6), , and the text gives . The in Table I is a different matter: the authors first remove rattlers (about 5%) and then assume to obtain this “precise” value. So agreeing with 4 does not independently prove that the exponent is one half.
Goodrich, Liu and Nagel (2012) refine the count: N disks in a periodic box need at least contacts for a positive bulk modulus, and the square-root law for the contact number fails when approaches one. Read Table I of the 2003 paper with that finite-size limit in mind.
Our recomputation: on the load-bearing network at the threshold, the contacts beyond the minimum count are 0 for 32 and 64 disks and at most 1 for 128 and 256 disks. Rattlers average 4.0% to 5.0%, in line with the paper’s roughly 5%. The contact-number exponent at 128 disks came out as 0.577 ± 0.009, against 0.49 in the paper.
Source: C. S. O’Hern · C. P. Goodrich
Worked example: checking our threshold statistics against Eqs. (3) and (4)
Question: in periodic boxes we took 24 random starts each for 32, 64, 128 and 256 disks. The full widths of the threshold distributions are 0.0259, 0.0148, 0.0129 and 0.00654, and the mean thresholds 0.827, 0.835, 0.838 and 0.839. Are they consistent with Eqs. (3) and (4), and roughly where would an infinite system jam?
Step one, the widths. The central values of Eq. (3), , give 0.0238, 0.0162, 0.0111 and 0.00758. Taking the ends of the uncertainties on and , the range allowed at 256 disks is 0.0048 to 0.0112, and our 0.00654 lies inside; the other three sizes also lie inside their ranges. Note that we estimate the full width as the standard deviation times , which assumes a Gaussian shape.
Step two, the extrapolation. In 2D , so 256 disks give , and the central values of Eq. (4) give . The mean threshold rises with size, so the peak approaches the limit from below and . Within the uncertainties of and , the correction lies between 0.0011 and 0.0045.
Conclusion: the widths are consistent with Eq. (3), and the extrapolated value of about 0.841 sits near the large-system value of 0.84 in Fig. 5(a). Two approximations remain: Eq. (4) concerns the peak of the distribution while we used the mean of 24 samples, and 256 disks is itself only a moderate size.
Source: C. S. O’Hern
A checklist for papers like this one
First, find the protocol: how states are made, what decides “jammed” and how tight the convergence is. Second, separate measurement from fitting: the location of Point J and the limiting contact number come from extrapolation or assumption. Third, find the range of validity: Eq. (3) holds for large systems only, and by the captions of Figs. 3 and 9 the 2D scaling data come from 1024 disks. Fourth, check captions against the text: when they disagree, as at Fig. 3, go with the table and the derivation, and note what you did not verify.
Fifth, when recomputing, state what counts as agreement. We required the widths to fall within the range allowed by the uncertainties of Eq. (3), and the contact exponent to fall within the of Table I. The first passed; the second, 0.577 against 0.49, did not. Both results are on record.
Source: C. S. O’Hern · C. P. Goodrich