RCP, s$_{rcp}$ . (f) This value is then plugged into Eq. (31a) to finish the calculation of s ( φ$_{j}$ ) by a final integration. The above procedure is repeated for different values of X$_{rlp}$ starting from infinite up to a finite value that will match the Shannon entropy as discussed below. B. Shannon entropy Entropy of jammed matter can be calculated in two different ways: (i) The entropy from fluctuation theory explained above. (ii) The entropy from information theory [23], so called ”Shannon entropy”, related to configurational disorder since topologically equivalent structures are considered as the same state. Shannon entropy attempts to measure the disorder in a string of information as defined in the seminal work of Shannon. This concept has been adapted to the measurement of the configurational entropy in physical systems defined through a contact network by Vink and Barkema [R. L. C. Vink, G. T. Barkema, Phys. Rev. Lett. 89 , 076405 (2002)]. In this work it was shown that the entropy obtained from the thermodynamic integration of fluctuations (or equivalently the specific heat) and the Shannon entropy are equivalent and accurately describe amorphous silicon and vitreous silica networks. The method has been extended to calculate the entropy of packings of granular materials in [23]. Below we explain the main details. The advantage of the Shannon entropy calculation over the thermodynamic integration is that it does not require a constant of integration as in Eq. (31). For each state of jammed matter, we associate a probability of occurrence p$_{i}$ to the state, which is calculated as follows. We use the Voronoi cell and Delaunay triangulation for each particle to define a Voronoi network by considering contacts when a Voronoi side is shared between two particles, and hence are Delaunay contacts. A graph is constructed as a cluster of n particles that are Delaunay contacts, and by means of graph automorphism [B. D. McKay, Nauty user’s guide (version 1.5), Tech. Rep. TR-CS-90-02, Australian National University (1990)] can be transformed into a standard form or “class” i of topologically equivalent graphs with a probability of occurrence p ( i ). In practice, we determine p ( i ) by extracting a large number m of clusters of size n from the system and count the number of times, f$_{i}$ , a cluster i is observed, such that: p ( i ) = f$_{i}$/m. (35) 31