arXiv subjects

Vladimir A. Baulin

Publications and source records attributed to Vladimir A. Baulin.

At least 19 recordsLinked to original sources

arXiv2026

Geometry-Encoded Hidden Orientational Memory

Materials usually remember their past through dissipative changes of structure or through a net order. Simulations of caged colloidal ellipsoids at a fluid interface reveal a purely orientational memory. The particles stay in place, the layer has no net alignment, and a pattern written by a field is retained by capillary interactions between neighbors. It lasts hundreds of times longer than a particle takes to reorient, while every particle keeps rotating. The layer also remembers sequence: when two strong fields act one after the other on the same particles, it records which came last.

cond-mat.soft↗

arXiv2025

Material-Based Intelligence: Self-organizing, Autonomous and Adaptive Cognition Embodied in Physical Substrates

The design of intelligent materials often draws parallels with the complex adaptive behaviors of biological organisms, where robust functionality stems from sophisticated hierarchical organization and emergent long-distance coordination among a myriad local components. Current synthetic materials, despite integrating advanced sensors and actuators, predominantly demonstrate only simple, pre-programmed stimulus-response functionalities, falling short of robustly autonomous intelligent behavior. These systems typically execute tasks determined by rigid design or external control, fundamentally lacking the intricate internal feedback loops, dynamic adaptation, self-generated learning, and genuine self-determination characteristic of biological agents. This perspective proposes a fundamentally different approach focusing on architectures where material-based intelligence is not pre-designed, but arises spontaneously from self-organization harnessing far-from-equilibrium dynamics. This work explores interdisciplinary concepts from material physics, chemistry, biology, and computation, identifying concrete pathways toward developing materials that not only react, but actively perceive, adapt, learn, self-correct, and potentially self-construct, moving beyond biomimicry to cultivate fully synthetic, self-evolving systems without external control. This framework outlines the fundamental requirements for, and constraints upon, future architectures where complex, goal-directed functionalities emerge synergistically from integrated local processes, distinguishing material-based intelligence from traditional hardware-software divisions. This demands that concepts of high-level goals and robust, replicable memory mechanisms are encoded and enacted through the material's inherent dynamics, inherently blurring the distinction between system output and process.

cond-mat.soft↗

arXiv2025

Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?

The concept of the 'agent' has profoundly shaped Artificial Intelligence (AI) research, guiding development from foundational theories to contemporary applications like Large Language Model (LLM)-based systems. This paper critically re-evaluates the necessity and optimality of this agent-centric paradigm. We argue that its persistent conceptual ambiguities and inherent anthropocentric biases may represent a limiting framework. We distinguish between agentic systems (AI inspired by agency, often semi-autonomous, e.g., LLM-based agents), agential systems (fully autonomous, self-producing systems, currently only biological), and non-agentic systems (tools without the impression of agency). Our analysis, based on a systematic review of relevant literature, deconstructs the agent paradigm across various AI frameworks, highlighting challenges in defining and measuring properties like autonomy and goal-directedness. We argue that the 'agentic' framing of many AI systems, while heuristically useful, can be misleading and may obscure the underlying computational mechanisms, particularly in Large Language Models (LLMs). As an alternative, we propose a shift in focus towards frameworks grounded in system-level dynamics, world modeling, and material intelligence. We conclude that investigating non-agentic and systemic frameworks, inspired by complex systems, biology, and unconventional computing, is essential for advancing towards robust, scalable, and potentially non-anthropomorphic forms of general intelligence. This requires not only new architectures but also a fundamental reconsideration of our understanding of intelligence itself, moving beyond the agent metaphor.

cs.AI↗

arXiv2025

Intelligent Soft Matter: Towards Embodied Intelligence

Intelligent soft matter stands at the intersection of materials science, physics, and cognitive science, promising to change how we design and interact with materials. This transformative field seeks to create materials that possess life-like capabilities, such as perception, learning, memory, and adaptive behavior. Unlike traditional materials, which typically perform static or predefined functions, intelligent soft matter dynamically interacts with its environment. It integrates multiple sensory inputs, retains experiences, and makes decisions to optimize its responses. Inspired by biological systems, these materials intend to leverage the inherent properties of soft matter: flexibility, self-evolving, and responsiveness to perform functions that mimic cognitive processes. By synthesizing current research trends and projecting their evolution, we present a forward-looking perspective on how intelligent soft matter could be constructed, with the aim of inspiring innovations in fields such as biomedical devices, adaptive robotics, and beyond. We highlight new pathways for integrating design of sensing, memory and action with internal low-power operations and discuss challenges for practical implementation of materials with "intelligent behavior". These approaches outline a path towards to more robust, versatile and scalable materials that can potentially act, compute, and "think" by their inherent intrinsic material behaviour beyond traditional smart technologies relying on external control.

cond-mat.soft↗

arXiv2025

Exploring Broad-Spectrum Antimicrobial Nanotopographies: Implications for Bactericidal, Antifungal, and Virucidal Surface Design

Inspired by the natural defence strategies of insect wings and plant leaves, nanostructured surfaces have emerged as a promising tool in various fields, including engineering, biomedical sciences, and materials science to combat bacterial contamination and disrupt biofilm formation. However, the development of effective antimicrobial surfaces against fungal and viral pathogens presents distinct challenges, necessitating tailored approaches. Here, we aimed to review the recent advancements of the use of nanostructured surfaces to combat microbial contamination, particularly focusing on their mechanobactericidal and antifungal properties, as well as their potential in mitigating viral transmission. We comparatively analysed the diverse geometries and nano-architectures of these surfaces and discussed their applications in various biomedical contexts, such as dental and orthopedic implants, drug delivery systems, and tissue engineering. Our review highlights the importance of preventing microbial attachment and biofilm formation, especially in the context of rising antimicrobial resistance and the economic impact of biofilms. We also discussed the latest progress in material science, particularly nanostructured surface engineering, as promising strategies for reducing viral transmission through surfaces. Overall, our findings underscore the significance of innovative strategies to mitigate microbial attachment and surface-mediated transmission, while also emphasizing the need for further interdisciplinary research in this area to optimize antimicrobial efficacy and address emerging challenges.

physics.bio-ph↗

arXiv2024

Machine Learning Reveals Large-scale Impact of Posidonia Oceanica on Mediterranean Sea Water

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existence of a robust correlation between the exact location of P. oceanica and water biogeochemical properties. The model's feature importance, showed that carbon-related variables as net biomass production and downward surface mass flux of carbon dioxide have their values altered in the areas with P. oceanica, which in turn can be used for indirect location of P. oceanica meadows. The study provides the evidence of the plant's ability to exert a global impact on the environment and underscores the crucial role of this plant in sea ecosystems, emphasizing the need for its conservation and management.

physics.ao-ph↗

arXiv2019

Neural network learns physical rules for copolymer translocation through amphiphilic barriers

Recent development in computer processing power leads to new paradigms of how problems in many-body physics and especially polymer physics can be addressed. GPU parallel processors can be employed to generate millions of independent configurations of polymeric molecules of heterogeneous sequence in complex environments at a second, and concomitant free-energy landscapes estimated. Resulting data bases that are complete in terms of polymer sequence and architecture are a powerful training basis for multi-layer artificial neural networks, whose internal representations will potentially lead to a new physical viewpoint in how sequence patterns are linked to effective polymer properties and response to the environment. In our example, we consider the translocation time of a copolymer through an amphiphilic bilayer membranes as a function of binary sequence of hydrophilic and hydrophobic units. First we demonstrate that massively parallel Rosenbluth sampling for all possible sequences of a polymer allows for meaningful dynamic interpretation in terms of the mean first escape times through the membrane. Second we train a multi-layer perceptron, and show by a systematic reduction of the training set to a narrow window of translocation times, that the neural network develops internal representations of the physical rules mapping sequence to translocation times. In particular, based on the narrow training set, the network predicts the correct order of magnitude of translocation times in a window that is more than 8 orders of magnitude wider than the training window.

cond-mat.soft↗

arXiv2017

GPU implementation of the Rosenbluth generation method for static Monte Carlo simulations

We present parallel version of Rosenbluth Self-Avoiding Walk generation method implemented on Graphics Processing Units (GPUs) using CUDA libraries. The method scales almost linearly with the number of CUDA cores and the method efficiency has only hardware limitations. The method is introduced in two realizations: on a cubic lattice and in real space. We find a good agreement between serial and parallel implementations and consistent results between lattice and real space realizations of the method for linear chain statistics. The developed GPU implementations of Rosenbluth algorithm can be used in Monte Carlo simulations and other computational methods that require large sampling of molecules conformations.

physics.comp-ph↗

arXiv2015

Mechanism of dynamic reorientation of cortical microtubules due to mechanical stress

Directional growth caused by gravitropism and corresponding bending of plant cells has been explored since 19th century, however, many aspects of mechanisms underlying the perception of gravity at the molecular level are still not well known. Perception of gravity in root and shoot gravitropisms is usually attributed to gravisensitive cells, called statocytes, which exploit sedimentation of macroscopic and heavy organelles, amyloplasts, to sense the direction of gravity. Gravity stimulus is then transduced into distal elongation zone, which is several mm far from statocytes, where it causes stretching. It is suggested that gravity stimulus is conveyed by gradients in auxin flux. We propose a theoretical model that may explain how concentration gradients and/or stretching may indirectly affect the global orientation of cortical microtubules, attached to the cell membrane and induce their dynamic reorientation perpendicular to the gradients. In turn, oriented microtubules arrays direct the growth and orientation of cellulose microfibrils, forming part of the cell external skeleton and determine the shape of the cell. Reorientation of microtubules is also observed in reaction to light in phototropism and mechanical bending, thus suggesting universality of the proposed mechanism.

physics.bio-ph↗

arXiv2014

General model of phospholipid bilayers in fluid phase within the single chain mean field theory

Coarse-grained model for saturated (DCPC, DLPC, DMPC, DPPC, DSPC) and unsaturated (POPC, DOPC) phospholipids is introduced within the Single Chain Mean Field theory. A single set of parameters adjusted for DMPC bilayers gives an adequate description of equilibrium and mechanical properties of a range of saturated lipid molecules that differ only in length of their hydrophobic tails and unsaturated (POPC, DOPC) phospholipids which have double bonds in the tails. A double bond is modeled with a fixed angle of 120 degrees, while the rest of the parameters are kept the same as saturated lipids. The thickness of the bilayer and its hydrophobic core, the compressibility and the equilibrium area per lipid correspond to experimentally measured values for each lipid, changing linearly with the length of the tail. The model for unsaturated phospholipids also fetches main thermodynamical properties of the bilayers. This model is used for an accurate estimation of the free energies of the compressed or stretched bilayers in stacks or multilayers and gives reasonable estimates for free energies. The proposed model may further be used for studies of mixtures of lipids, small molecule inclusions, interactions of bilayers with embedded proteins.

cond-mat.soft↗

arXiv2012

Critical adsorption controls translocation of polymer chains through lipid bilayers and permeation of solvent

Monte Carlo simulations using an explicit solvent model indicate a new pathway for translocation of a polymer chain through a lipid bilayer. We consider a polymer chain composed of repeat units with a given hydrophobicity and a coarse-grained model of a lipid bilayer in the self-organized liquid state. By varying the degree of hydrophobicity the chain undergoes an adsorption transition with respect to the lipid bilayer. Close to the transition point, at a properly balanced hydrophobicity of the chain, the membrane becomes transparent with respect to the chain. At the same time the solvent permeability of the bilayer is strongly increased in the region close to adsorbed chain. Our results indicate that the critical point of adsorption of the polymer chain interacting with the fluctuating lipid bilayer could play a key role for the translocation of molecules though biological membranes.

cond-mat.soft↗

arXiv2012

Self-assembly of spherical interpolyelectrolyte complexes from oppositely charged polymers

The formation of inter-polyelectrolyte complexes from the association of oppositely charged polymers in an electrolyte is studied. The charged polymers are linear oppositely charged polyelectrolytes, with possibly a neutral block. This leads to complexes with a charged core, and a more dilute corona of dangling chains, or of loops (flower-like structure). The equilibrium aggregation number of the complexes (number of polycations m+ and polyanions m-) is determined by minimizing the relevant free energy functional, the Coulombic contribution of which is worked out within Poisson-Boltzmann theory. The complexes can be viewed as colloids that are permeable to micro-ionic species, including salt. We find that the complexation process can be highly specific, giving rise to very localized size distribution in composition space (m+,m-).

cond-mat.soft↗

arXiv2012

Degradation versus self-assembly of block copolymer micelles

The stability of micelles self-assembled from block copolymers can be altered by the degradation of the blocks. Slow degradation shifts the equilibrium size distribution of block copolymer micelles and change their properties. Quasi-equilibrium scaling theory shows that the degradation of hydrophobic blocks in the core of micelles destabilize the micelles reducing their size, while the degradation of hydrophilic blocks forming coronas of micelles favors larger micelles and may, at certain conditions, induce the formation of micelles from individual chains.

cond-mat.soft↗

arXiv2012

Surface patterning of carbon nanotubes can enhance their penetration through a phospholipid bilayer

Nanotube patterning may occur naturally upon the spontaneous self-assembly of biomolecules onto the surface of single-walled carbon nanotubes (SWNTs). It results in periodically alternating bands of surface properties, ranging from relatively hydrophilic to hydrophobic, along the axis of the nanotube. Single Chain Mean Field (SCMF) theory has been used to estimate the free energy of systems in which a surface patterned nanotube penetrates a phospholipid bilayer. In contrast to un-patterned nanotubes with uniform surface properties, certain patterned nanotubes have been identified that display a relatively low and approximately constant system free energy (10 kT) as the nanotube traverses through the bilayer. These observations support the hypothesis that the spontaneous self-assembly of bio-molecules on the surface of SWNTs may facilitate nanotube transduction through cell membranes.

physics.bio-ph↗

arXiv2012

Biomolecule surface patterning may enhance membrane association

Under dehydration conditions, amphipathic Late Embryogenesis Abundant (LEA) proteins fold spontaneously from a random conformation into alpha-helical structures and this transition is promoted by the presence of membranes. To gain insight into the thermodynamics of membrane association we model the resulting alpha-helical structures as infinite rigid cylinders patterned with hydrophobic and hydrophilic stripes oriented parallel to their axis. Statistical thermodynamic calculations using Single Chain Mean Field (SCMF) theory show that the relative thickness of the stripes controls the free energy of interaction of the alpha-helices with a phospholipid bilayer, as does the bilayer structure and the depth of the equilibrium penetration of the cylinders into the bilayer. The results may suggest the optimal thickness of the stripes to mimic the association of such protein with membranes.

physics.bio-ph↗

arXiv2011

Equilibrium insertion of nanoscale objects into phospholipid bilayers

Certain membrane proteins, peptides, nanoparticles and nanotubes have rigid structure and fixed shape. They are often viewed as spheres and cylinders with certain surface properties. Single Chain Mean Field theory is used to model the equilibrium insertion of nanoscale spheres and rods into the phospholipid bilayer. The equilibrium structures and the resulting free energies of the nano-objects in the bilayer allow to distinguish different orientations in the bilayer and estimate the energy barrier of insertion.

physics.bio-ph↗

arXiv2010

Can a Carbon Nanotube Pierce through a Phospholipid Bilayer?

Great efficiency to penetrate into living cells is attributed to carbon nanotubes due to a number of direct and indirect observations of carbon nanotubes inside the cells. However, a direct evidence of physical translocation of nanotubes through phospholipid bilayers and the exact microscopic mechanism of their penetration into cells are still lacking. In order to test one of the inferred translocation mechanisms, namely the spontaneous piercing through the membrane induced only by thermal motion, we calculate the energy cost associated with the insertion of a carbon nanotube into a model phospholipid bilayer using the Single Chain Mean Field theory which is particularly suitable for the accurate measurements of equilibrium free energies. We find that the energy cost of the bilayer rupture is quite high compared to the energy of thermal motion. This conclusion may indirectly support other energy dependent translocation mechanisms such as, for example, endocytosis.

cond-mat.soft↗

arXiv2010

Aggregation of amphiphilic polymers in the presence of adhesive small colloidal particles

The interaction of amphiphilic polymers with small colloids, capable to reversibly stick onto the chains, is studied. Adhesive small colloids in solution are able to dynamically bind two polymer segments. This association leads to topological changes in the polymer network configurations, such as looping and cross-linking, although the reversible adhesion permits the colloid to slide along the chain backbone. Previous analyses only consider static topologies in the chain network. We show that the sliding degree of freedom ensures the dominance of small loops, over other structures, giving rise to a new perspective in the analysis of the problem. The results are applied to the analysis of the equilibrium between colloidal particles and star polymers, as well as to block copolymer micelles. The results are relevant for the reversible adsorption of silica particles onto hydrophilic polymers, used in the process of formation of mesoporous materials of the type SBA or MCM, cross-linked cyclodextrin molecules threading on the polymers and forming the structures known as polyrotaxanes. Adhesion of colloids on the corona of the latter induce micellization and growth of larger micelles as the number of colloids increases, in agreement with experimental data.

cond-mat.soft↗