arXiv subjects

Stefano Zapperi

Publications and source records attributed to Stefano Zapperi.

At least 19 recordsLinked to original sources

arXiv2026

Elastic softening and fracture in randomly perforated solids

We study the mechanical response of quasi-brittle polymethyl methacrylate (PMMA) specimens containing controlled random distributions of laser-cut holes. Tensile tests combined with digital image correlation reveal a nearly linear decrease of the Young's modulus with porosity, but with a softening rate far exceeding classical effective medium theory and the Hashin-Shtrikman bound. The extrapolated critical porosity at which the modulus vanishes is well below the 2D percolation threshold, indicating that ideal cylindrical void models fail to capture the observed behavior. Microscopy shows irregular pore geometries and frequent coalescence, which effectively act as crack-like defects and strongly enhance compliance. The rupture stress distributions are well described by a Weibull model accounting for both load-bearing area reduction and stress concentration at hole edges. Digital image correlation reveals heterogeneous but non-localized deformation, with strain increasingly correlated with the hole pattern, indicating a growing influence of defect-induced stress concentrations. These results highlight the dominant role of defect morphology in governing stiffness degradation and fracture statistics in porous quasi-brittle materials.

cond-mat.mtrl-sci↗

arXiv2025

Allometric scaling of brain activity explained by avalanche criticality

Allometric scaling laws, such as Kleiber's law for metabolic rate, highlight how efficiency emerges with size across living systems. The brain, with its characteristic sublinear scaling of activity, has long posed a puzzle: why do larger brains operate with disproportionately lower firing rates? Here we show that this economy of scale is a universal outcome of avalanche dynamics. We derive analytical scaling laws directly from avalanche statistics, establishing that any system governed by critical avalanches must exhibit sublinear activity-size relations. This theoretical prediction is then verified in integrate-and-fire neuronal networks at criticality and in classical self-organized criticality models, demonstrating that the effect is not model-specific but generic. The predicted exponents align with experimental observations across mammal species, bridging dynamical criticality with the allometry of brain metabolism. Our results reveal avalanche criticality as a fundamental mechanism underlying Kleiber-like scaling in the brain.

q-bio.NC↗

arXiv2025

Fiber bundle model of thermally activated creep failure

An equal load sharing fiber bundle model for thermally activated breakdown is developed using transition state theory to describe the rate of elementary failures. The lifetime distribution, average, variance and their asymptotic limits for uniform fiber failure thresholds are derived and found to be in excellent agreement with simulations. The asymptotic scaling with regards to the number of fibers matches analytical approximations in the low temperature limit derived by Roux and co-workers for a model of thermal breakdown by stationary Gaussian noise. For the case of randomly distributed fiber failure strengths, the lifetime distribution is derived as a multidimensional integral with no closed form solution. Simulations with different fiber strength distributions indicate that, in the limit of large fiber numbers, the statistics of bundle lifetimes shows a similar asymptotic scaling for distributed and for uniform thresholds. Fiber breakage by thermal activation occurs in avalanches triggered by individual thermally activated failure events, and the asymptotic avalanche size distribution obtained from the simulations matches earlier theoretical results derived for quasistatic loading.

cond-mat.stat-mech↗

arXiv2025

Fracture toughness and auxeticity in disordered metamaterials

Auxetic metamaterials are commonly thought to exhibit favorable mechanical properties, notably high energy absorption. Here we investigate disordered metamaterials obtained from random beam networks by optimizing simultaneously auxeticity and the energy absorbed before fracture. By giving different weights to these optimization targets, we demonstrate that the optimal configurations are connected along a Pareto front where high auxeticity implies comparatively low energy absorption and vice versa. We study the mechanical properties of the resulting metamaterials and characterize the different deformation modes obtained for distinct optimization targets. The simulation and optimization results are validated by comparison with the deformation behavior of additively manufactured samples. Our work provides an illustration of the potentials and limitations of multi-objective optimization in the design of disordered mechanical metamaterials

cond-mat.dis-nn↗

arXiv2023

Quasi-localized modes in crystalline high entropy alloys

High Entropy Alloys (HEAs) are designed by mixing multiple metallic species in nearly the same amount to obtain crystalline or amorphous materials with exceptional mechanical properties. Here we use molecular dynamics simulations to investigate the role of positional and compositional disorder in determining the low-frequency vibrational properties of CrMnFeCoNi HEAs. Our results show that the expected dependence of the density of states on the frequency as $D(\omega)\sim\omega^4$ is recovered for amorphous HEAs and is also observed for partially crystallized alloys with deviations that depend on the degree of crystallization. We find that the quasi-localized vibrations are still visible in crystalline HEAs, albeit suppressed compared to the corresponding amorphous alloys. Our work offers a unified perspective to describe HEA mechanical properties in terms of their vibrational density of states.

cond-mat.dis-nn↗

arXiv2022

Predicting Creep Failure by Machine Learning -- Which Features Matter?

Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated from simulations of a novel, brittle random fuse model (RFM), as well as elasto-plastic finite element simulations (FEM) of a stochastic plasticity model with damage, both models considering stochastic thermally activated damage/failure processes in disordered materials. Fuse networks are generated with hierarchical and nonhierarchical architectures. Random forests - a specific ML algorithm - allow us to measure the feature importance through a feature's average error reduction. RFM simulation data are found to become more predictable with increasing system size and temperature. Increasing the load or the scatter in local materials properties has the opposite effect. Damage accumulation in these models proceeds in stochastic avalanches, and statistical signatures such as avalanche rate or magnitude have been discussed in the literature as predictors of incipient failure. However, in the present study such features proved of no measurable use to the ML models, which mostly rely on global or local strain for prediction. This suggests the strain as viable quantity to monitor in future experimental studies as it is accessible via digital image correlation.

cond-mat.mtrl-sci↗

arXiv2022

Size effects in micro and nanoscale materials fracture

Micro and nanoscale materials have remarkable mechanical properties, such as enhanced strength and toughness, but usually display sample-to-sample fluctuations and non-trivial size effects, a nuisance for engineering applications and an intriguing problem for science. Our understanding of size-effects in small-scale materials has progressed considerably in the past few years thanks to a growing number of experimental measurements on carbon based nanomaterials, such as graphene carbon nanotubes, and on crystalline and amorphous micro/nanopillars and micro/nanowires. At the same time, increased computational power allowed atomistic simulations to reach experimentally relevant sample sizes. From the theoretical point of view, the standard analysis and interpretation of experimental and computational data relies on traditional extreme value theories developed decades ago for macroscopic samples, with recent work extending some of the limiting assumptions of the original theories. In this review, we discuss the recent experimental and numerical literature on micro and nanoscale fracture size effects, illustrate existing theories pointing out their advantages and limitations and finally provide a tutorial for analyzing fracture data from micro and nanoscale samples. We discuss a broad spectrum of materials but provide at the same time a unifying theoretical framework that should be helpful for materials scientists working on micro and nanoscale mechanics.

cond-mat.mtrl-sci↗

arXiv2022

Predicting the failure of two-dimensional silica glasses

Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate failure predictions are becoming possible even for strongly disordered solids, but the sheer number of parameters used in the process renders a physical interpretation of the results impossible. Here we address this issue and use machine learning methods to predict the failure of simulated two dimensional silica glasses from their initial undeformed structure. We then exploit Gradient-weighted Class Activation Mapping (Grad-CAM) to build attention maps associated with the predictions, and we demonstrate that these maps are amenable to physical interpretation in terms of topological defects and local potential energies. We show that our predictions can be transferred to samples with different shape or size than those used in training, as well as to experimental images. Our strategy illustrates how artificial neural networks trained with numerical simulation results can provide interpretable predictions of the behavior of experimentally measured structures.

cond-mat.mtrl-sci↗

arXiv2021

Universal Density of Low Frequency States in Silica Glass at Finite Temperatures

The theoretical understanding of the low-frequency modes in amorphous solids at finite temperature is still incomplete. The study of the relevant modes is obscured by the dressing of inter-particle forces by collision-induced momentum transfer that is unavoidable at finite temperatures. Recently, it was proposed that low frequency modes of vibrations around the {\em thermally averaged} configurations deserve special attention. In simple model glasses with bare binary interactions, these included quasi-localized modes whose density of states appears to be universal, depending on the frequencies as $D(\omega) \sim \omega^4$, in agreement with the similar law that is obtained with bare forces at zero temperature. In this work, we report investigations of a model of silica glass at finite temperature; here the bare forces include binary and ternary interactions. Nevertheless we can establish the validity of the universal law of the density of quasi-localized modes also in this richer and more realistic model glass.

cond-mat.dis-nn↗

arXiv2021

Extreme value theory and the St. Petersburg paradox in the failure statistics of wires

The fracture stress of materials typically depends on the sample size and is traditionally explained in terms of extreme value statistics. A recent work reported results on the carrying capacity of long polyamide and polyester wires and interpret the results in terms of a probabilistic argument known as the St. Petersburg paradox. Here, we show that the same results can be better explained in terms of extreme value statistics. We also discuss the relevance of rate dependent effects.

cond-mat.stat-mech↗

arXiv2020

Universal low-frequency vibrational modes in silica glasses

It was recently shown that different simple models of glass formers with binary interactions define a universality class in terms of the density of states of their quasi-localized low-frequency modes. Explicitly, once the hybridization with standard Debye (extended) modes is avoided, a number of such models exhibit a universal density of state, depending on the mode frequencies as $D(\omega) \sim \omega^4$. It is unknown however how wide is this universality class, and whether it also pertains to more realistic models of glass formers. To address this issue we present analysis of the quasi-localized modes in silica, a network glass which has both binary and ternary interactions. We conclude that in 3-dimensions silica exhibits the very same frequency dependence at low frequencies, suggesting that this universal form is a generic consequence of amorphous glassiness.

cond-mat.soft↗

arXiv2020

Unjamming of active rotators

Active particle assemblies can exhibit a wide range of interesting dynamical phases depending on internal parameters such as density, adhesion strength or self-propulsion. Active self-rotations are rarely studied in this context, although they can be relevant for active matter systems, as we illustrate by analyzing the motion of Chlamydomonas reinhardtii algae under different experimental conditions. Inspired by this example, we simulate the dynamics of a system of interacting active disks endowed with active torques. At low packing fractions, adhesion causes the formation of small rotating clusters, resembling those observed when algae are stressed. At higher densities, the model shows a jamming to unjamming transition promoted by active torques and hindered by adhesion. Our results yield a comprehensive picture of the dynamics of active rotators, providing useful guidance to interpret experimental results in cellular systems where rotations might play a role.

cond-mat.soft↗

arXiv2020

Automatic Design of Mechanical Metamaterial Actuators

Mechanical metamaterials actuators achieve pre-determined input--output operations exploiting architectural features encoded within a single 3D printed element, thus removing the need of assembling different structural components. Despite the rapid progress in the field, there is still a need for efficient strategies to optimize metamaterial design for a variety of functions. We present a computational method for the automatic design of mechanical metamaterial actuators that combines a reinforced Monte Carlo method with discrete element simulations. 3D printing of selected mechanical metamaterial actuators shows that the machine-generated structures can reach high efficiency, exceeding human-designed structures. We also show that it is possible to design efficient actuators by training a deep neural network, eliminating the need for lengthy mechanical simulations. The elementary actuators devised here can be combined to produce metamaterial machines of arbitrary complexity for countless engineering applications.

cond-mat.soft↗

arXiv2019

Elementary plastic events in amorphous silica

Plastic instabilities in amorphous materials are often studied using idealized models of binary mixtures that do not capture accurately molecular interactions and bonding present in real glasses. Here we study atomic scale plastic instabilities in a three dimensional molecular dynamics model of silica glass under quasi-static shear. We identify two distinct types of elementary plastic events, one is a standard quasi-localized atomic rearrangement while the second is a bond breaking event that is absent in simplified models of fragile glass formers. Our results show that both plastic events can be predicted by a drop of the lowest non-zero eigenvalue of the Hessian matrix that vanishes at a critical strain. Remarkably, we find very high correlation between the associated eigenvectors and the non-affine displacement fields accompanying the bond breaking event, predicting the locus of structural failure. Both eigenvectors and non-affine displacement fields display an Eshelby-like quadrupolar structure for both failure modes, rearrangement or bond-breaking. Our results thus clarify the nature of atomic scale plastic instabilities in silica glasses providing useful information for the development of mesoscale models of amorphous plasticity.

cond-mat.dis-nn↗

arXiv2019

Protein driven lipid domain nucleation in biological membranes

Lipid rafts are heterogeneous dynamic lipid domains of the cell membranes that are involved in several biological processes, like protein and lipids specific transport and signaling. Our understanding of lipid raft formation is still limited, due to the transient and elusive nature of these domains {\it in vivo}, in contrast to the stable phase-separated domains observed in artificial membranes. Inspired by experimental findings highlighting the relevance of transmembrane proteins for lipid rafts, we investigate lipid domain nucleation by coarse-grained molecular dynamics and Ising model simulations. We find that the presence of a transmembrane protein can trigger lipid domain nucleation in a flat membrane from an otherwise mixed lipid phase. Furthermore, we study the role of the lipid domain in the diffusion of the protein showing that its mobility is hindered by the presence of the raft. The results of our coarse-grained molecular dynamics and Ising model simulations thus coherently support the important role played by transmembrane proteins in lipid domain formation and stability.

physics.bio-ph↗

arXiv2019

Oscillatory Instabilities in 3-Dimensional Frictional Granular Matter

The dynamics of amorphous granular matter with frictional interactions cannot be derived in general from a Hamiltonian and therefore displays oscillatory instabilities stemming from the onset of complex eigenvalues in the stability matrix. These instabilities were discovered in the context of one and two dimensional systems, while the three dimensional case was never studied in detail. Here we fill this gap by deriving and demonstrating the presence of oscillatory instabilities in a three dimensional granular packing. We study binary assemblies of spheres of two sizes interacting via classical Hertz and Mindlin force laws for the longitudinal and tangent interactions, respectively. We formulate analytically the stability matrix in 3D and observe that a couple of complex eigenvalues emerges at the onset of the instability as in the case of frictional disks in two-dimensions. The dynamics then shows oscillatory exponential growth in the Mean-Square-Displacement, followed by a catastrophic event. The generality of these results for any choice of forces that break the symplectic Hamiltonian symmetry is discussed.

cond-mat.stat-mech↗

arXiv2018

Density scaling in the mechanics of a disordered mechanical meta-material

Nature provides examples of self-assemble lightweight disordered network structures with remarkable mechanical properties which are desirable for many applications purposes but challenging to reproduce artificially. Previous experimental and computational studies investigated the mechanical responses of random network structures focusing on topological and geometrical aspects in terms of variable connectivity or probability to place beam elements. However for practical purposes an ambitious challenge is to design new materials with the possibility to taylor their mechanical features such as stiffness. Here, we design a two dimensional disordered mechanical meta-material exhibiting unconventional stiffness-density scaling in the regime where both bending and stretching are relevant for deformation. In this regime, the mechanical meta-material covers a wide interval of the Young modulus - density plane, simultaneously exhibiting high critical stress and critical strain. Our results, supported by finite element simulations, provides the guiding principles to design on demand disordered metamaterials, bridging the gap between artificial and naturally occurring materials.

cond-mat.soft↗

arXiv2018

Strain Modulation of Graphene by Nanoscale Substrate Curvatures: A Molecular View

Spatially nonuniform strain is important for engineering the pseudomagnetic field and band structure of graphene. Despite the wide interest in strain engineering, there is still a lack of control on device-compatible strain patterns due to the limited understanding of the structure-strain relationship. Here, we study the effect of substrate corrugation and curvature on the strain profiles of graphene via combined experimental and theoretical studies of a model system: graphene on closely packed SiO2 nanospheres with different diameters (20-200 nm). Experimentally, via quantitative Raman analysis, we observe partial adhesion and wrinkle features and find that smaller nanospheres induce larger tensile strain in graphene, theoretically, molecular dynamics simulations confirm the same microscopic structure and size dependence of strain and reveal that a larger strain is caused by a stronger, inhomogeneous interaction force between smaller nanospheres and graphene. This molecular-level understanding of the strain mechanism is important for strain engineering of graphene and other two-dimensional materials.

cond-mat.mes-hall↗