arXiv subjects

Massimiliano Di Ventra

Publications and source records attributed to Massimiliano Di Ventra.

At least 19 recordsLinked to original sources

arXiv2026

Engineering non-Markovianity from spectral shaping of bosonic and fermionic baths

Non-Markovianity is the property that a physical system's future state depends on its previous dynamical history, namely the system has memory of its past. It is a key resource for quantum technologies but it is difficult to engineer without specific strategies. Here, we show that introducing a band gap in the environmental spectral density of both bosonic and fermionic baths provides a direct route to tuning non-Markovianity over a wide range. We demonstrate this effect by means of numerical simulations across bosonic and fermionic environments and discuss experimental tests of our predictions.

quant-ph↗

arXiv2026

Optically Tunable Threshold Switching and Thermally Activated Transport in Planar Ag/MAPbI$_3$ Thin Single-Crystal Devices

Halide perovskites have enabled major advances in optoelectronics, extending well beyond photovoltaics. Their mixed ionic-electronic conduction, once regarded as detrimental to device stability, is increasingly viewed as a functional degree of freedom for memory and neuromorphic-inspired devices, especially when coupled to external stimuli such as light. Specifically, single crystals are attractive models because they suppress grain-boundary effects and microstructural disorder that can mask intrinsic transport and interfacial mechanisms in polycrystalline films. Here, we report the growth of thin methylammonium lead iodide (MAPbI$_3$) single crystals by a space-confined method and their integration into planar two-terminal devices with directly deposited Ag contacts. At room temperature, the devices exhibit ultra-low dark currents ($10^{-13}-10^{-12}$ A) and negligible hysteresis in the dark. Under illumination, the current increases due to photogeneration and the I-V characteristics develop a pronounced polarity-dependent hysteresis and a threshold-like transition between two conductance states. Temperature-dependent dark measurements (300-400 K) show thermionically activated, contact-influenced transport and a weakly varying normalized hysteresis metric. Together with the back-to-back Schottky-diode analysis and control devices using more inert contact materials, these results support a transport model in which Ag/perovskite interfaces play a central role and the hysteretic response is influenced by coupled interfacial and ionic processes.

cond-mat.mtrl-sci↗

arXiv2026

Scientific discovery as meta-optimization: a combinatorial optimization case study

Scientific discovery is fundamentally an optimization problem, defined by a vast "state space" of theories and experiments, and an evaluation criterion based on quality, novelty, and validity. Large language models (LLMs) have enabled automated exploration of this space, but we argue that simultaneous modification of the evaluation criteria is equally important. Here, we propose formalizing research as meta-optimization, where the optimization objective itself is also being optimized. Our key contribution is "consensus objective aggregation," where LLM-generated objective functions are combined via correlation-weighted voting, yielding a stable, self-correcting evaluation criterion that evolves as understanding deepens. We apply this framework to algorithm discovery for 3-SAT problems based on digital MemComputing machines, reducing the baseline scaling with problem size $N$ from $\sim N^{2.51}$ to $\sim N^{1.33}$ and delivering a $\sim 67\times$ speedup on the largest instances tested. As a problem-agnostic framework, we hope this approach will considerably aid scientific discovery.

cs.AI↗

arXiv2026

A Critical Assessment of the Brain Criticality Hypothesis

A major unresolved question in Neuroscience is: What is the origin of the observed scale-invariant correlations in neural activity? Many researchers support the ``criticality hypothesis,'' which proposes that the brain operates near criticality, optimizing various information processing functions. However, the nature and behavior of criticality in cortical systems are still unclear. Alternatively, this opinion paper highlights that the coupling between neurons and slowly varying energetic resources, which may act as a form of ``memory,'' alone may be sufficient to generate a robust phase of neural activity with scale-invariant correlations. This memory-induced long-range order phase could provide a more natural explanation of the existing experimental data than the criticality hypothesis.

physics.bio-ph↗

arXiv2026

The Supersymmetric Origin of Chaos and its Hidden Topological Order

Dynamical chaos is a term that encompasses a wide range of nonlinear phenomena such as turbulence, neuronal avalanches, weather patterns, and many others. However, despite much work in the field of chaos, its fundamental physical origin still remains not fully understood. In this perspective we report on recent studies showing that chaos is the realization of one of the most fundamental principles in physics: spontaneous symmetry breaking also known as spontaneous ordering. In the present context, the symmetry involved is a topological supersymmetry inherent to all continuous-time (stochastic) dynamical systems. Chaos is then truly a manifestation of order of topological origin potentially encoding a sort of long-range information hidden beneath its apparent unpredictability. We finally argue that this point of view may have far-reaching implications well beyond chaotic dynamics.

nlin.CD↗

arXiv2026

Memory-induced long-range order drag

Recent research has shown that memory, in the form of slow degrees of freedom, can induce a phase of long-range order (LRO) in locally-coupled fast degrees of freedom, producing power-law distributions of avalanches. In fact, such memory-induced LRO (MILRO) arises in a wide range of physical systems. Here, we show that MILRO can be transferred to coupled systems that have no memory of their own. As an example, we consider a stack of layers of spins with local feedforward couplings: only the first layer contains memory, while downstream layers are memory-free and locally interacting. Analytical arguments and simulations reveal that MILRO can indeed drag across the layers, enabling downstream layers to sustain intra-layer LRO despite having neither memory nor long-range interactions. This establishes a simple, yet generic mechanism for propagating collective activity through media without fine tuning to criticality, with testable implications for neuromorphic systems and laminar information flow in the brain cortex.

cond-mat.stat-mech↗

arXiv2025

CrSe_2 and CrTe_2 Monolayers as Efficient Air Pollutants Nanosensors

Nanosensors are critical in environmental monitoring, industrial safety, and public health by detecting specific hazardous gases like CO, NO, SO_2, and CH_4 at trace levels. This study uses density functional theory (DFT) calculations to examine the gas-sensing capabilities of chromium diselenide (CrSe_2) and chromium ditelluride (CrTe_2) monolayers through their structural and electronic responses to gas adsorption. Adsorption energy analysis shows that Te vacancy-induced CrTe_2 (VTe-CrTe_2) exhibits the strongest binding with energies of -1.52, -1.79, and -1.61 eV for CO, NO, and SO_2, respectively. Similarly, CrSe_2 has its values of -1.13, -1.17, -0.90, and -1.12 eV for CO, NO, SO_2, and CH_2, respectively, indicating suitability for reversible sensing. This study also investigates how substitutional doping of Ge, Sb, and Sn influences the sensing mechanism of CrSe_2 and CrTe_2 monolayers. Density of states (DOS) analysis highlights notable electronic changes around the Fermi level, especially in VTe-CrTe_2 and Sb/Sn-doped CrTe_2, confirming their enhanced sensing abilities. Charge density difference analysis shows significant charge redistribution, with CrTe_2 experiencing stronger charge transfer effects than CrSe_2. Variations in electrostatic potential and work function further demonstrate the higher sensitivity of CrTe_2, particularly in its defective and doped forms, confirming its status as a superior material for gas sensing applications.

cond-mat.mtrl-sci↗

arXiv2025

Phase-Space Engineering and Collective Dynamics in Memcomputing

Digital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding ordinary differential equations depend on a few hyper-parameters that define both the system's relevant time scales and its phase-space geometry. Using numerical simulations on a prototypical DMM, we analyze the role of these physical parameters in engineering the phase space to either help or hinder the solution search by DMMs. We find that the DMM explores its phase space efficiently for a wide range of parameters, aided by the system-wide correlations in their fast degrees of freedom that emerge dynamically due to coupling with the (slow) memory degrees of freedom. In this regime, the time it takes for the system to find a solution scales well as the number of variables increases. When these hyper-parameters are chosen poorly, the system navigates its phase space far less efficiently. However, we find that, in many cases, collective behavior persists even when the phase-space exploration process is inefficient. This behavior only disappears if the memories are made to evolve as quickly as the fast degrees of freedom. This study points to the important role of memory and hyper-parameters in engineering the DMMs' phase space for optimal computational efficiency.

physics.comp-ph↗

arXiv2025

On the solvable-unsolvable transition due to noise-induced chaos in digital memcomputing

Digital memcomputing machines (DMMs) have been designed to solve complex combinatorial optimization problems. Since DMMs are fundamentally classical dynamical systems, their ordinary differential equations (ODEs) can be efficiently simulated on modern computers. This provides a unique platform to study their performance under various conditions. An aspect that has received little attention so far is how their performance is affected by the numerical errors in the solution of their ODEs and the physical noise they would be naturally subject to if built in hardware. Here, we analyze these two aspects in detail by varying the integration time step (numerical noise) and adding stochastic perturbations (physical noise) into the equations of DMMs. We are particularly interested in understanding how noise induces a chaotic transition that marks the shift from successful problem-solving to failure in these systems. Our study includes an analysis of power spectra and Lyapunov exponents depending on the noise strength. The results reveal a correlation between the instance solvability and the sign of the ensemble averaged mean largest Lyapunov exponent. Interestingly, we find a regime in which DMMs with positive mean largest Lyapunov exponents still exhibit solvability. Furthermore, the power spectra provide additional information about our system by distinguishing between regular behavior (peaks) and chaotic behavior (broadband spectrum). Therefore, power spectra could be utilized to control whether a DMM operates in the optimal dynamical regime. Overall, we find that the qualitative effects of numerical and physical noise are mostly similar, despite their fundamentally different origin.

nlin.CD↗

arXiv2025

Memory in neural activity: long-range order without criticality

The "criticality hypothesis", based on observed scale-free correlations in neural activity, posits that the brain operates at a critical point of transition between two phases. However, the validity of this hypothesis is still debated. Here, employing a commonly used model of cortical dynamics, we find that a phase of long-range order (LRO) in neural activity may be induced by memory (time non-locality) without invoking criticality. The cortical dynamics model contains fast and slow time scales that govern the neural and resource (memory) dynamics, respectively. When the resource dynamics are sufficiently slow, we observe an LRO phase, which manifests in avalanche size and duration probability distributions that are fit well by power laws. When the slow and fast time scales are no longer sufficiently distinct, LRO is destroyed. Since this LRO phase spans a wide range of parameters, it is robust against perturbations, unlike critical systems.

physics.bio-ph↗

arXiv2025

Chirality-induced Spin-Orbit Coupling and Spin Selectivity

We show that a spinor traveling along a one-dimensional helical path develops a spin-orbit coupling as a result of the curvature of the path. We estimate the magnitude of the associated spin polarization and obtain values typical of many helical molecular structures that showcase the Chirality-induced Spin Selectivity (CISS) effect. We find that this chirality-induced spin-orbit coupling ($χ$-SOC), in conjunction with broken time-reversal symmetry, may be an important ingredient for the microscopic underpinning of the CISS phenomenon.

cond-mat.mes-hall↗

arXiv2025

Mechanical Force-Driven Charge Redistribution for Hydrogen Release at Ambient Conditions in Transition Metal-Intercalated Bilayer Graphene

Transition-metal (TM) atom-functionalized nanomaterials are promising candidates for hydrogen storage due to their ability to adsorb multiple hydrogen molecules through Kubas interactions. However, achieving efficient hydrogen desorption at ambient conditions remains a critical challenge for practical use. Here, we present a novel approach to modulate the desorption temperature of hydrogen in TM-intercalated bilayer graphene (BLG) using external mechanical forces. By employing first-principles density functional theory (DFT) and thermodynamic occupancy probability calculations, we demonstrate that adjusting the interlayer distance allows for precise control over the interaction energy of H2, thereby facilitating its desorption at ambient conditions. Complete hydrogen desorption occurs when the interlayer distance is reduced below 4.7 Å, 5.3 Å, and 5.1 Å for Sc-, Ti-, and V-intercalated BLG, respectively. Our findings suggest that external mechanical forces can effectively bring hydrogen occupancy to zero by minimizing charge transfer from the TM d-orbitals to H2 antibonding orbitals. Notably, while the total charge transferred from the TM atoms remains nearly constant at varying interlayer distances, its redistribution between the graphene layers and H2 fine-tunes the interaction strength. This approach can be extended to large interlayer distances, as supported by recent experiments on graphene oxide membranes [ACS Nano 12, 9309 (2018)]. Furthermore, recent experimental advances in noble gas and alkali metal intercalation in BLG highlight the potential of this approach to overcome the long-standing challenge of high desorption temperatures in TM-functionalized layered nanomaterials.

cond-mat.mtrl-sci↗

arXiv2025

A Generative Neural Annealer for Black-Box Combinatorial Optimization

We propose a generative, end-to-end solver for black-box combinatorial optimization that emphasizes both sample efficiency and solution quality on NP problems. Drawing inspiration from annealing-based algorithms, we treat the black-box objective as an energy function and train a neural network to model the associated Boltzmann distribution. By conditioning on temperature, the network captures a continuum of distributions--from near-uniform at high temperatures to sharply peaked around global optima at low temperatures--thereby learning the structure of the energy landscape and facilitating global optimization. When queries are expensive, the temperature-dependent distributions naturally enable data augmentation and improve sample efficiency. When queries are cheap but the problem remains hard, the model learns implicit variable interactions, effectively "opening" the black box. We validate our approach on challenging combinatorial tasks under both limited and unlimited query budgets, showing competitive performance against state-of-the-art black-box optimizers.

cs.LG↗

arXiv2025

Mixed-Mode In-Memory Computing: Towards High-Performance Logic Processing In A Memristive Crossbar Array

In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable device behavior, which affects data accuracy and efficiency. In this work, the authors present a new computing method that combines two types of operations,those based on electrical resistance and those based on voltage, within each memory cell. This design improves reliability and avoids the need for expensive current measurements. A new software tool also helps automate the design process, supporting highly parallel operations in dense two-dimensional memory arrays. The approach balances speed and space, making it practical for advanced computing tasks. Demonstrations include a digital adder and a key part of the encryption module, showing both strong performance and accuracy. This work offers a new direction for reliable and efficient in-memory computing systems with real-world applications.

physics.app-ph↗

arXiv2025

Memory effects in pulsed optomechanical systems

Memory, understood as time non-locality, is a fundamental property of any physical system, whether classical or quantum, and has important applications in a wide variety of technologies. In the context of quantum technologies, systems with memory can be used in quantum information, communication, and sensing. Here, we demonstrate that cavity optomechanical systems driven by a pulsed laser can operate as programmable quantum memory elements. By engineering the adiabatic and non-adiabatic pulses, particularly the Gaussian and sinusoidal, we induce and control diverse memory phenomena such as dynamical hysteresis, quantized phononic transitions, and distinct energy-storing responses. Within a mean-field approach, we derive the analytical and numerical criteria under which the photonic and phononic observables manifest the memory effects in strongly driven regimes. The memory effects are quantified through a dimensionless geometric form factor, which provides a versatile metric to characterize the memory efficiency. Our protocol is readily compatible with the current optomechanical platforms, highlighting the new possibilities for advanced memory functionalities in quantum technologies.

quant-ph↗

arXiv2024

Implementation of digital MemComputing using standard electronic components

Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non-locality), have proven to be a robust and scalable unconventional computing approach for solving a wide variety of combinatorial optimization problems. However, most of the research so far has focused on the numerical simulations of the equations of motion of DMMs. This inevitably subjects time to discretization, which brings its own (numerical) issues that would be otherwise absent in actual physical systems operating in continuous time. Although hardware realizations of DMMs have been previously suggested, their implementation would require materials and devices that are not so easy to integrate with traditional electronics. Addressing this, our study introduces a novel hardware design for DMMs, utilizing readily available electronic components. This approach not only significantly boosts computational speed compared to current models but also exhibits remarkable robustness against additive noise. Crucially, it circumvents the limitations imposed by numerical noise, ensuring enhanced stability and reliability during extended operations. This paves a new path for tackling increasingly complex problems, leveraging the inherent advantages of DMMs in a more practical and accessible framework.

cs.ET↗

arXiv2024

Collective dynamics and long-range order in thermal neuristor networks

In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed "thermal neuristors." These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

cond-mat.dis-nn↗

arXiv2024

Roadmap for Unconventional Computing with Nanotechnology

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

cs.ET↗