arXiv subjects

Bortolo Matteo Mognetti

Publications and source records attributed to Bortolo Matteo Mognetti.

At least 19 recordsLinked to original sources

arXiv2026

Colloidal layer deposition with a controllable number of layers and compositional order

We design a system with a binary suspension of colloids and a surface that triggers the self-assembly of crystallites with a finite thickness. The proposed design allows controlling the number of layers forming the aggregate and constrains the two types of particles to lie on different planes. These functionalities are achieved by decorating the colloids and the surface with multiple DNA oligomers featuring specific interactions. Similarly to what was proposed in previous studies, the surface triggers a chain of reactions between DNA oligomers, leading to localized self-assembly. System parameters control the thickness of the aggregates. In this work, compositional order is achieved by engineering the reaction kinetics between DNA oligomers in a way that limits interactions between colloids of the same type. We validate our design using theory and reaction-diffusion simulation algorithms, which capture the multibody nature of the interactions. This work demonstrates how engineering the reaction kinetics provides a new tool for controlling the morphology of aggregates assembled by DNA.

cond-mat.soft↗

arXiv2026

Evolutionary chemical learning in dimerization networks

We present a framework for chemical learning based on Competitive Dimerization Networks (CDNs) - systems in which multiple molecular species, e.g., proteins, DNA oligomers, or RNA oligomers, reversibly bind to form dimers. We show numerically that these networks can, in principle, be trained in vitro through directed evolution, enabling the implementation of complex learning tasks such as multiclass classification without digital hardware or prior knowledge of all microscopic association constants. Each molecular species functions analogously to a neuron, with binding affinities acting as tunable synaptic weights. A training protocol involving mutation, selection, and amplification of DNA-based components allows CDNs to robustly discriminate among noisy input patterns. The resulting classifiers exhibit strong output contrast and high mutual information between input and output, especially when guided by a contrast-enhancing loss function. Comparative analysis with in silico gradient descent training reveals closely correlated performance. These results establish CDNs as a promising platform for analog physical computation, bridging synthetic biology and machine learning, and advancing the development of adaptive, energy-efficient molecular computing systems.

cond-mat.stat-mech↗

arXiv2026

Equilibrium Propagation for Non-Conservative Systems

Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and learning. In its original formulation it is limited to conservative systems, $\textit{i.e.}$ to dynamics which derive from an energy function. Given their applications, it is important to extend EP to non-conservative systems, $\textit{i.e.}$ systems with non-reciprocal interactions. Previous attempts to generalize EP to such systems failed to compute the exact gradient of the cost function. Here we propose a framework that extends EP to arbitrary non-conservative systems, including feedforward networks. We keep the key property of equilibrium propagation, namely the use of stationary states both for inference and learning. However, we modify the dynamics in the learning phase by a term proportional to the non-reciprocal part of the interaction so as to obtain the exact gradient of the cost function. This algorithm can also be derived using a variational formulation that generates the learning dynamics through an energy function defined over an augmented state space. Numerical experiments show that this algorithm achieves better performance and learns faster than previous proposals.

cs.LG↗

arXiv2024

Equilibrium Propagation: the Quantum and the Thermal Cases

Equilibrium propagation is a recently introduced method to use and train artificial neural networks in which the network is at the minimum (more generally extremum) of an energy functional. Equilibrium propagation has shown good performance on a number of benchmark tasks. Here we extend equilibrium propagation in two directions. First we show that there is a natural quantum generalization of equilibrium propagation in which a quantum neural network is taken to be in the ground state (more generally any eigenstate) of the network Hamiltonian, with a similar training mechanism that exploits the fact that the mean energy is extremal on eigenstates. Second we extend the analysis of equilibrium propagation at finite temperature, showing that thermal fluctuations allow one to naturally train the network without having to clamp the output layer during training. We also study the low temperature limit of equilibrium propagation.

quant-ph↗

arXiv2023

Using Markov transition matrices to generate trial configurations in Markov chain Monte Carlo simulations

We propose a new Markov chain Monte Carlo method in which trial configurations are generated by evolving a state, sampled from a prior distribution, using a Markov transition matrix. We present two prototypical algorithms and derive their corresponding acceptance rules. We first identify the important factors controlling the quality of the sampling. We then apply the method to the problem of sampling polymer configurations with fixed endpoints. Applications of the proposed method range from the design of new generative models to the improvement of the portability of specific Monte Carlo algorithms, like configurational-bias schemes.

cond-mat.stat-mech↗

arXiv2022

Sliding across a surface: particles with fixed and mobile ligands

A quantitative model of the mobility of functionalized particles at the interface is pivotal to understanding important systems in biology and nanotechnology. In this work, we investigate the emerging dynamics of particles anchored through ligand-receptor bridges to functionalized surfaces. We consider systems with reversible bridges in which ligand-receptor pairs bind/unbind with finite reaction rates. For a given set of bridges, the particle can explore a tiny fraction of the surface as the extensivity of the bridges is finite. We show how at time scales longer than the bridges' lifetime, the averaged position of the particle diffuses away from its initial value. We distill our findings into two analytic equations for the sliding diffusion constant of particles carrying mobile and fixed ligands. We quantitatively validate our theoretical predictions using reaction-diffusion simulations. Our results, along with recent literature, will allow inferring the microscopic parameters at play in complex biological systems from experimental trajectories.

cond-mat.soft↗

arXiv2020

Adaptable DNA interactions regulate surface triggered self assembly

DNA-mediated multivalent interactions between colloidal particles have been extensively applied for their ability to program bulk phase behaviour and dynamic processes. Exploiting the competition between different types of DNA-DNA bonds, here we experimentally demonstrate the selective triggering of colloidal self-assembly in the presence of a functionalised surface, which induces changes in particle-particle interactions. Besides its relevance to the manufacturing of layered materials with controlled thickness, the intrinsic signal-amplification features of the proposed interaction scheme make it valuable for biosensing applications.

cond-mat.soft↗

arXiv2020

Self-assembly of finite-sized colloidal aggregates

One of the challenges of self-assembling finite-sized colloidal aggregates with a sought morphology is the necessity of precisely sorting the position of the colloids at the microscopic scale to avoid the formation of off-target structures. Microfluidic platforms address this problem by loading into single droplets the exact amount of colloids entering the targeted aggregate. Using theory and simulations, in this paper, we validate a more versatile design allowing us to fabricate different types of finite-sized aggregates, including colloidal molecules or core-shell clusters, starting from finite density suspensions of isotropic colloids in bulk. In our model, interactions between particles are mediated by DNA linkers with mobile tethering points, as found in experiments using DNA oligomers tagged with hydrophobic complexes immersed into supported bilayers. By fine-tuning the strength and the number of the different types of linkers, we prove the possibility of controlling the morphology of the aggregates, in particular, the valency of the molecules and the size of the core-shell clusters. In general, our design shows how multivalent interactions can lead to microphase separation in equilibrium conditions.

cond-mat.soft↗

arXiv2019

Translational and Rotational Dynamics of Colloidal Particles Interacting through Reacting Linkers

Much work has studied effective interactions between micron-sized particles carrying linkers forming reversible, inter-particle linkages. These studies allowed understanding the equilibrium properties of colloids interacting through ligand-receptor interactions. Nevertheless, understanding the kinetics of multivalent interactions remains an open problem. Here, we study how molecular details of the linkers, such as the reaction rates at which inter-particle linkages form/break, affect the relative dynamics of pairs of cross-linked colloids. Using a simulation method tracking single binding/unbinding events between complementary linkers, we rationalize recent experiments and prove that particles' interfaces can move across each other while being cross-linked. We clarify how, starting from diffusing colloids, the dynamics become arrested when increasing the number of inter-particle linkages or decreasing the reaction rates. Before getting arrested, particles diffuse through rolling motion. The ability to detect rolling motion will be useful to shed new light on host-pathogen interactions.

cond-mat.soft↗

arXiv2018

Surface-triggered cascade reactions between DNA linkers direct self-assembly of colloidal crystals of controllable thickness

Functionalizing colloids with reactive DNA linkers is a versatile way of programming self-assembly. DNA selectivity provides direct control over colloid-colloid interactions allowing the engineering of structures such as complex crystals or gels. However, self-assembly of localized and finite structures remains an open problem with many potential applications. In this work, we present a system in which functionalized surfaces initiate a cascade reaction between linkers leading to self-assembly of crystals with a controllable number of layers. Specifically, we consider colloidal particles functionalized by two families of complementary DNA linkers with mobile anchoring points, as found in experiments using emulsions or lipid bilayers. In bulk, intra-particle linkages formed by pairs of complementary linkers prevent the formation of inter-particle bridges and therefore colloid-colloid aggregation. However, colloids interact strongly with the surface given that the latter can destabilize intra-particle linkages. When in direct contact with the surface, colloids are activated, meaning that they feature more unpaired DNA linkers ready to react. Activated colloids can then capture and activate other colloids from the bulk through the formation of inter-particle linkages. Using simulations and theory, validated by existing experiments, we clarify the thermodynamics of the activation and binding process and explain how particle-particle interactions, within the adsorbed phase, weaken as a function of the distance from the surface. The latter observation underlies the possibility of self-assembling finite aggregates with controllable thickness and flat solid-gas interfaces. Our design suggests a new avenue to fabricate heterogeneous and finite structures.

cond-mat.soft↗

arXiv2018

Steric interactions between mobile ligands facilitate complete wrapping in passive endocytosis

Receptor-mediated endocytosis is an ubiquitous process through which cells internalize biological or synthetic nanoscale objects, including viruses, unicellular parasites, and nanomedical vectors for drug or gene delivery. In passive endocytosis the cell plasma membrane wraps around the "invader" particle driven by ligand-receptor complexation. By means of theory and numerical simulations, here we demonstrate how particles decorated by freely diffusing and non-mutually-interacting (ideal) ligands are significantly more difficult to wrap than those where ligands are either immobile or interact sterically with each other. Our model rationalizes the relationship between uptake mechanism and structural details of the invader, such as ligand size, mobility and ligand/receptor affinity, providing a comprehensive picture of pathogen endocytosis and helping the rational design of efficient drug delivery vectors.

physics.bio-ph↗

arXiv2018

Efficient sampling of reversible cross-linking polymers: Self-assembly of single-chain polymeric nanoparticles

We present a new simulation technique to study systems of polymers functionalized by reactive sites that bind/unbind forming reversible linkages. Functionalized polymers feature self-assembly and responsive properties that are unmatched by systems lacking selective interactions. The scales at which the functional properties of these materials emerge are difficult to model, especially in the reversible regime where such properties result from many binding/unbinding events. This difficulty is related to large entropic barriers associated with the formation of intra-molecular loops. In this work we present a simulation scheme that sidesteps configurational costs by dedicated Monte Carlo moves capable of binding/unbinding reactive sites in a single step. Cross-linking reactions are implemented by trial moves that reconstruct chain sections attempting, at the same time, a dimerization reaction between pairs of reactive sites. The model is parametrized by the reaction equilibrium constant of the reactive species free in solution. This quantity can be obtained by means of experiments or atomistic/quantum simulations. We use the proposed methodology to study self-assembly of single--chain polymeric nanoparticles, starting from flexible precursors carrying regularly or randomly distributed reactive sites. During a single run, almost all pairs of reactive monomers interact at least once. We focus on understanding differences in the morphology of chain nanoparticles when linkages are reversible as compared to the well studied case of irreversible reactions. Intriguingly, we find that the size of regularly functionalsized chains, in good solvent conditions, is non-monotonous as a function of the degree of functionalization. We clarify how this result follows from excluded volume interactions and is peculiar of reversible linkages and regular functionalizations.

cond-mat.soft↗

arXiv2013

A simple analytical formula for the free-energy of ligand-receptor mediated interactions

Recently \1, we presented a general theory for calculat- ing the strength and properties of colloidal interactions mediated by ligand-receptor bonds (such as those that bind DNA-coated colloids). In this communication, we derive a surprisingly simple analytical form for the inter- action free energy, which was previously obtainable only via a costly numerical thermodynamic integration. As a result, the computational effort to obtain potentials of in- teraction is significantly reduced. Moreover, we can gain insight from this analytic expression for the free energy in limiting cases. In particular, the connection of our general theory to other previous specialised approaches is now made transparent. This important simplification will significantly broaden the scope of our theory.

cond-mat.soft↗

arXiv2008

Spherically averaged versus angle-dependent interactions in quadrupolar fluids

Employing simplified models in computer simulation is on the one hand often enforced by computer time limitations but on the other hand it offers insights into the molecular properties determining a given physical phenomenon. We employ this strategy to the determination of the phase behaviour of quadrupolar fluids, where we study the influence of omitting angular degrees of freedom of molecules via an effective spherically symmetric potential obtained from a perturbative expansion. Comparing the liquid-vapor coexistence curve, vapor pressure at coexistence, interfacial tension between the coexisting phases, etc., as obtained from both the models with the full quadrupolar interactions and the (approximate) isotropic interactions, we find discrepancies in the critical region to be typically (such as in the case of carbon dioxide) of the order of 4%. However, when the Lennard-Jones parameters are rescaled such that critical temperatures and critical densities of both models coincide with the experimental results, almost perfect agreement between the above-mentioned properties of both models is obtained. This result justifies the use of isotropic quadrupolar potentials. We present also a detailed comparison of our simulations with a combined integral equation/density functional approach and show that the latter provides an accurate description except for the vicinity of the critical point.

cond-mat.stat-mech↗

arXiv2008

Third virial coefficient for 4-arm and 6-arm star polymers

We discuss the computation of the third virial coefficient in polymer systems, focusing on an additional contribution absent in the case of monoatomic fluids. We determine the interpenetration ratio and several quantities that involve the third virial coefficient for star polymers with 4 and 6 arms in the good-solvent regime, in the limit of a large degree of polymerization.

cond-mat.soft↗

arXiv2008

Efficient prediction of thermodynamic properties of quadrupolar fluids from simulation of a coarse-grained model: The case of carbon dioxide

Monte Carlo simulations are presented for a coarse-grained model of real quadrupolar fluids. Molecules are represented by particles interacting with Lennard-Jones forces plus the thermally averaged quadrupole-quadrupole interaction. The properties discussed include the vapor-liquid coexistence curve, the vapor pressure along coexistence, and the surface tension. The full isotherms are also accessible over a wide range of temperatures and densities. It is shown that the critical parameters (critical temperature, density, and pressure) depend almost linearly on a quadrupolar parameter $q=Q^{*4} /T^*$, $Q^*$ is the reduced quadrupole moment of the molecule and $T^*$ the reduced temperature. The model can be applied to a variety of small quadrupolar molecules. We focus on carbon dioxide as a test case, but consider nitrogen and benzene, too. Experimental critical temperature, density and quadrupolar moment are sufficient to fix the parameters of the model. The resulting agreement with experiments is excellent and marks a significant improvement over approaches which neglect quadrupolar effects. The same coarse-grained model was also applied in the framework of Perturbation Theory (PT) in the Mean Spherical Approximation (MSA). As expected, the latter deviates from the Monte Carlo results in the critical region, but is reasonably accurate at lower temperatures.

cond-mat.stat-mech↗

arXiv2007

Two-parameter model predictions and theta-point crossover for linear-polymer solutions

We consider the first few virial coefficients of the osmotic pressure, the radius of gyration, the hydrodynamic radius, and the end-to-end distance for a monodisperse polymer solution. We determine the corresponding two-parameter model functions which parametrize the crossover between the good-solvent and the ideal-chain behavior. These results allow us to predict the osmotic pressure and the polymer size in the dilute regime in a large temperature region above the theta-point.

cond-mat.soft↗

arXiv2007

Infra-red divergences in the large-N expansion

We investigate a vectorial O(N) model with a generic nearest-neighbor interaction W(\bsigma_i\cdot \bsigma_j) (depending on {\cal N} tunable parameters), a Yukawa (and Gross Neveu) model with N_f fermions at finite temperature and the vectorial ϕ^6 model, in the large N (N_f) limit. All this models exhibit a Mean Field critical point for N=\infinity. When 1/N fluctuations are included, infra red divergences appear near the critical point. In the framework of a generalized 1/N expansion we show that these divergences are related to a universal crossover mechanism between the Mean Field universality class (N=\infinity) and the nonclassical one for N<\infinity. For the generic nearest-neighbor interaction multicritical points are also investigated.

hep-lat↗