arXiv subjects

Srikanth Sastry

Publications and source records attributed to Srikanth Sastry.

At least 19 recordsLinked to original sources

arXiv2026

TheoPhysEval: Assessing AI agents at autonomous theoretical physics problem solving

We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved by a world expert in about a day or less but would take considerably longer for experts in adjacent specialties. We test the ability of off-the-shelf AI agents to tackle these problems autonomously, without human hints. When given access to tools available in a realistic setting, including the ability to search the literature and write scripts for numerical and symbolic work, the best-performing agent, GPT-6-Astra harnessed in Codex, obtains the final answers with a near-perfect accuracy of 98% at an average cost under USD 3 per problem. On average, agents show only small changes in accuracy when denied web search and access to background research papers, suggesting significant memorization of the literature. These results suggest that state-of-the-art agents can accurately perform concrete calculations required across theoretical physics research. We comment on the broader implications of these findings and discuss possible explanations for the current lack of agent-led breakthroughs in theoretical physics despite their outstanding problem-solving abilities.

hep-th↗

arXiv2026

Early Signatures of Memorization in Diffusion Models via Basin Geometry and Cyclic Denoising

Diffusion models generalize early in training and later reproduce individual training samples. Standard tests detect memorization only once one-shot generation produces near-copies, leaving a released model unaudited until its outputs fail. We show that memorization is encoded in the geometry of the learned energy landscape before it appears in generated samples, a state we call latent memorization. Using score divergence and basin volume, we find that localized basins form around training samples and separate them from held-out samples before the first memorized sample appears, with an onset that follows the same $O(n)$ scaling as the memorization time. We probe these basins with cyclic denoising, which repeatedly applies partial noising and denoising. Under the exact empirical score, we prove that cycling started near an isolated training sample recovers it and returns to it over any finite number of cycles with high probability. In trained models, cycling recovers training images from CelebA and CIFAR-10 checkpoints whose one-shot samples contain no copies, and at a CelebA checkpoint with 0.1% one-shot copies, 500 cycles raise the memorized fraction above 30%. Cycling also reveals degenerate attractors that match no single training image and fade as training proceeds, so residence in a basin does not by itself imply memorization. These findings hold on a Gaussian mixture, CelebA, and CIFAR-10 across optimizers, architectures, noise schedules, and training-set sizes, and extend to off-the-shelf Stable Diffusion v1.4, where the cycled conditional-unconditional divergence gap separates memorized from non-memorized prompts with an AUC of 0.944 and a TPR of 0.866 at 1% FPR. More broadly, what a diffusion model has memorized is a property of the geometry and stability of its learned distribution, and assessing it requires examining this structure rather than generated outputs alone.

cs.LG↗

arXiv2026

Fatigue failure in two-dimensional glasses under cyclic shear deformation

We investigate fatigue failure in two-dimensional (2D) model glasses under cyclic shear deformation using atomistic simulations. We find that the number of cycles to failure diverges as a power law as the strain amplitude approaches the fatigue limit, with an exponent close to $-1$, in contrast to the exponent of $-2$ reported in three dimensions (3D). A failure exponent of $-1$ has also recently been observed in a 2D elastoplastic model, suggesting an interesting dependence on spatial dimensionality that needs to be rationalized. Measures of accumulated plastic activity, including dissipated work and non-affine displacements, exhibit scaling with the failure time that is consistent with results in 3D, and indicate a robust connection between damage accumulation and failure. To probe the origins of the variability of failure times, we perform isoconfigurational {\it seeded} simulations in which a localized soft region is introduced. While such seeding constrains the spatial location of failure, the distribution of failure times remains broad. These findings extend recent results in 3D concerning fatigue failure times to 2D glasses, including their relation to accumulated plasticity and apparent stochasticity, while also revealing a key difference, namely the exponent describing the divergence of the failure times.

cond-mat.soft↗

arXiv2026

Stochasticity of fatigue failure times in sheared glasses

Fatigue failure occurs when a solid is subjected to repeated, cyclic loading. Glasses subjected to cyclic shear deformation have recently been investigated using computer simulations and theoretical models, to characterize and rationalize the dependence of the number of cycles to failure, depending on the properties of the glasses, and the deformation amplitude. The average number of cycles to failure has been observed to diverge as the strain amplitude approaches the so-called fatigue limit from above. In this work, rather than the average times themselves, we investigate by computer simulations the distribution of fatigue failure times, in model glasses subjected to cyclic shear deformation and in an elasto-plastic model. In particular, we observe in atomistic simulations that the standard deviation of the logarithm of failure times are proportional to their mean values, with the proportionality constant decreasing as the system size increases, indicating a sharper distribution of failure times. Using a finite-element-based elasto-plastic model, we observe similar behavior and perform a system-size analysis showing that the ratio of the standard deviation to the mean tends toward zero in the thermodynamic limit. Such distributions, rather than arising solely from the distribution of disorder in the samples that have been subjected to cyclic deformation, appear to arise from the intrinsic stochasticity of the failure process, which we analyze through a stochastic damage accumulation model.

cond-mat.stat-mech↗

arXiv2026

Elastoplastic Modelling of Cyclic Shear Deformation of Amorphous Solids

We develop an energy-landscape based elasto-plastic model to understand the behaviour of amorphous solids under uniform and cyclic shear. Amorphous solids are modeled as being composed of mesoscopic sub-volumes, each of which may occupy states - termed mesostates -- drawn from a specified distribution. The energies of the mesostates under stress free conditions determine their stability range with respect to applied strain, and their plastic strain, at which they are stress free, forms an important additional property. Under applied global strain, mesostates that reach their stability limits transition to other permissible mesostates. Barring such transitions, which encompass plastic deformations that the solid may undergo, mesostates are treated as exhibiting linear elastic behavior, and the interactions between mesoscopic blocks are treated using the finite element method. The model reproduces known phenomena under uniform and cyclic shear, such as the brittle-to-ductile crossover with annealing and the Bauschinger effect for uniform shear, qualitative features of the yielding diagram under cyclic shear including the change in yielding behaviour with the degree of annealing, across a `threshold level', and dynamic phenomena such as the divergence of failure times on approach to the yield point and the non-monotonic evolution of the local yield rate. In addition to these results, we discuss the dependence of the observed behaviour on model choices, and open questions highlighted by our work.

cond-mat.soft↗

arXiv2026

Yielding behaviour of glasses under shear deformation at constant pressure

Computer simulations of yielding of glasses under shear have typically been performed under constant volume, strain controlled protocols. However, volumetric effects, such as the dilatancy associated with plastic rearrangements, and the observed reduction of density in shear bands, make it interesting to consider constant pressure shear protocols. We present a computational investigation on the nature of yielding of glasses under constant-pressure conditions, for different pressures. For uniform shear, the stress-strain curves at different pressures differ only by the stress scale. We find stable shear bands under cyclic shear whose steady-state width increases with an increase in external pressure, with density within shear bands being lower compared to the average values reached. Cyclically sheared well annealed glasses yield with a discontinuous dilation at the yield point, whereas the poorly annealed glasses undergo compaction before yielding accompanied by dilation. The external pressure influences the quantitative mechanical response of the glasses, but the qualitative behaviour is similar at different pressures, and remains the same as that of yielding at the constant-volume strain-controlled conditions. We discuss directions along with further investigations may be pursued, based on the results presented.

cond-mat.soft↗

arXiv2025

Recent Advances in Metallic Glasses

This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in experimental characterization, additive manufacturing, multiscale modeling approaches, and the growing role of machine learning in understanding and designing these complex materials. On the experimental side, we highlight measurements of thermophysical properties of supercooled liquids via fast chip calorimetry and enhancements in mechanical properties through rejuvenation treatments. This work underscores the crucial role of short-range order and medium-range order in controlling metallic glass mechanical properties. Recent progress in structural probes allows in situ observations of deformation mechanisms, positioning the field well to further advance our understanding of mechanical properties. Additive manufacturing of metallic glasses is discussed as one encouraging new manufacturing route for metallic glasses. We examine laser powder-bed fusion process physics and the central trade-off between amorphicity and densification, including heat affected zone devitrification and defects formation, together with emerging mitigation strategies and applications. On the theoretical and simulation side, we review advances in nanoscale, mesoscale, and continuum modeling of metallic glasses that have led to promising approaches by which multiscale schemes can incorporate data sourced from atomic-scale simulations. These efforts have helped to elucidate the connection between the glass structure and mechanical and rheological responses. We also cover the development of machine learning interatomic potentials for metallic glasses, along with machine learning driven prediction of glass forming ability and inverse design methods. Finally, challenges and directions for future research are presented and discussed.

cond-mat.mtrl-sci↗

arXiv2025

Self-organization, Memory and Learning: From Driven Disordered Systems to Living Matter

Disordered systems subject to a fluctuating environment can self-organize into a complex history-dependent response, retaining a memory of the driving. In sheared amorphous solids, self-organization is established by the emergence of a persistent system of mechanical instabilities that can repeatedly be triggered by the driving, leading to a state of high mechanical reversibility. As a result of self-organization, the response of the system becomes correlated with the dynamics of its environment, which can be viewed as a sensing mechanism of the system's environment. Such phenomena emerge across a wide variety of soft matter systems, suggesting that they are generic and hence may depend very little on the underlying specifics. We review self-organization in driven amorphous solids, concluding with a discussion of what self-organization in driven disordered systems can teach us about how simple organisms sense and adapt to their changing environments.

cond-mat.soft↗

arXiv2025

Packing, Phase Separation and Interface Compatibility in Reversibly Crosslinked Polymers

Vitrimers are a class of crosslinked polymer that are capable of undergoing bond exchange reactions, allowing structural reorganization while maintaining overall network integrity. Two key features that are particularly relevant when this vitrimer concept is used to compatibilize immiscible polymer blends are how they affect the (i) bulk polymer density and (ii) interfacial activity of the crosslink groups. To probe these issues, we model both a bulk polymer melt and a thin film of a polymer melt both with explicit small molecular crosslinkers, in the associative limit, i.e., when the number of crosslinks are fixed. We show that the bulk density and the distribution of stickers within a polymer matrix is strongly influenced by their size and interactions with the base polymer. Specifically, when the crosslinkers are chemically compatible with the base polymer, then the overall packing fraction increases, regardless of crosslinker size, while it decreases when crosslinkers are incompatible with the polymers. Similarly, the crosslinkers segregate preferentially to the polymer-air interface when they are incompatible with the polymer chains, leading to a reduction interfacial tension. Thus, these incompatible crosslinkers should help in affecting both the miscibility of polymer blends, and also their compatibility by creating copolymer structures at the interface. These results demonstrate the key role of crosslinker-polymer interactions and crosslinker size on the structural and interfacial properties of vitrimer melts.

cond-mat.soft↗

arXiv2025

Structure-Dynamics Correlation and Its Link to Fragility and Dynamic Heterogeneity

Understanding the connection between structure, dynamics, and fragility, the rate at which the relaxation time grows with decreasing temperature, is central to unravelling the glass transition. Fragility is often associated with dynamic heterogeneity, implying that if structure influences dynamics, more fragile systems should exhibit stronger structure dynamics correlations. In this study, we test the generality of this assumption using: Lennard Jones (LJ) and Weeks Chandler Andersen (WCA) systems, where fragility is tuned via density, and a modified LJ (q,p) system, where fragility is varied by changing the potential softness. We define a structural order parameter based on a mean field caging potential and analyse energy barriers at both macroscopic and microscopic levels. While the macroscopic free energy barrier slope correlates with fragility, the microscopic free energy barrier does not show a consistent trend. Instead, it exhibits a strong correlation with a structure dynamics correlation measure obtained from isoconfigurational ensemble simulations. Interestingly, the two systems showing the highest structure dynamics correlation, LJ at rho = 1.1 and the (8,5) model, are respectively the least and most fragile within their classes. These systems exhibit broad mobility distributions, large non Gaussian parameters, yet low four point susceptibilities, suggesting a decoupling between spatial correlation length and mobility contrast. Both systems lie in the enthalpy dominated regime and are close to the spinodal, pointing to mechanical instability as a source of heterogeneity. Our results reveal that structure dynamics correlation is more closely linked to the contrast in individual particle mobility than to the spatial extent of dynamic correlations that typically scale with fragility.

cond-mat.soft↗

arXiv2025

Microphase Separation Controls the Dynamics of Associative Vitrimers

Vitrimers are a class of polymers characterized by dynamic covalent networks, where specific monomer units, which are know as stickers, form reversible crosslinks that enable network rearrangement without loss of overall connectivity. The conventional wisdom is that the sticker dynamics control chain relaxation behavior, and hence the mechanical properties of associative vitrimers where the number of crosslinked sticker pairs is precisely constant over time. Instead, here we show that the chemical differences between sticker groups and nonsticky chain monomers can cause them to microphase separate. Under these conditions, the slow exchange of a sticker from one microphase to an adjacent one controls relaxation behavior. Controlling sticker aggregation is thus a key to tailoring the properties of these polymers with immediate relevance to a circular polymer economy.

cond-mat.soft↗

arXiv2025

Coarse grained descriptions of the dynamics of yielding of amorphous solids under cyclic shear

Recent computer simulations reveal several intriguing features in the evolution of properties of amorphous solids subjected to repeated cyclic shear deformation. These include the divergence of the number of cycles to reach steady states as the yielding point is approached, a non-monotonic change of properties with cycles, and the possibility of a spectrum of frozen states. Theoretical attempts to capture these properties through simple models, including the Ehrenfest model describing a random walk in a confining potential, have met partial success. Here, we show that incorporating the influence of mechanical noise through a feedback term leads to a genuine dynamical transition with characteristics reflecting those of yielding. Coarse graining the dynamics into a small number of variables leads to new insights regarding the dynamics of yielding.

cond-mat.stat-mech↗

arXiv2025

Rouse Mode Analysis of Chain Relaxation in Reversibly Crosslinked Polymer Melts

Polymer melts with chains undergoing reversible crosslinking have distinctively favorable dynamic properties, e.g., self healing and reprocessability. In these situations there are two relevant elementary time scales: the segmental and the sticker association times. A convenient framework to model these situations is the sticky Rouse model and here we perform hybrid moleculear dynamics (MD) Monte Carlo (MC) simulations to examine its relevance. In agreement with the underpinning idea discussed above we find that reversibly crosslinked chains show two distinct modes of relaxation behavior depending on the magnitude of bond lifetimes. For bond lifetimes shorter than the chain end-to-end relaxation time, the polymers exhibit essentially Rouse like dynamics, but with an apparently increased local friction relative to the non-sticky analog. For longer bond lifetimes, the chains exhibit two modes of relaxation: the faster mode is independent of bond lifetime, but the slower mode is controlled by it. However, these slower mode results are not consistent with the predictions of the sticky Rouse model. Our Rouse mode analysis as a function of chain length, N, imply that this is likely a result of the relatively short chain length employed, but they nevertheless suggest that theories need to include these small chain effects if they are to be relevant to experimental systems with short chains following Rouse dynamics.

cond-mat.soft↗

arXiv2024

A correspondence between Hebbian unlearning and steady states generated by nonequilibrium dynamics

The classic paradigms for learning and memory recall focus on strengths of synaptic couplings and how these can be modulated to encode memories. In a previous paper [A. K. Behera, M. Rao, S. Sastry, and S. Vaikuntanathan, Physical Review X 13, 041043 (2023)], we demonstrated how a specific non-equilibrium modification of the dynamics of an associative memory system can lead to increase in storage capacity. In this work, using analytical theory and computational inference schemes, we show that the dynamical steady state accessed is in fact similar to those accessed after the operation of a classic unsupervised scheme for improving memory recall, Hebbian unlearning or ``dreaming". Together, our work suggests how nonequilibrium dynamics can provide an alternative route for controlling the memory encoding and recall properties of a variety of synthetic (neuromorphic) and biological systems.

cond-mat.dis-nn↗

arXiv2024

Fatigue failure in glasses under cyclic shear deformation

Solids subjected to repeated cycles of stress or deformation can fail after several cycles, a phenomenon termed fatigue failure. Although intensely investigated for a wide range of materials owing to its obvious practical importance, a microscopic understanding of the initiation of fatigue failure continues to be actively pursued, in particular for soft and amorphous materials. We investigate fatigue failure for glasses subjected to cyclic shear deformation through computer simulations. We show that, approaching the so-called fatigue limit, failure times display a power law divergence, at variance with commonly used functional forms, and exhibit strong dependence on the degree of annealing of the glasses. We explore several measures of damage, based on quantification of plastic rearrangements and on dissipated energy. Strikingly, the fraction of particles that undergo plastic rearrangements, and a percolation transition they undergo, are predictive of failure. We also find a robust power law relationship between accumulated damage, quantified by dissipated energy or non-affine displacements, and the failure times, which permits prediction of failure times based on behavior in the initial cycles. These observations reveal salient new microscopic features of fatigue failure and suggest approaches for developing a full microscopic picture of fatigue failure in amorphous solids.

cond-mat.soft↗

arXiv2024

Entropic Cohesion in Vitrimers

Vitrimers are polymer networks that can undergo bond exchange reactions. They dynamically rearrange their structures while maintaining their overall integrity, thus resulting in unique properties such as self-healing, reprocessability, shape memory and adaptability. Here, we show that the introduction of dynamic bonds directly impacts the polymer density. For a limiting case, where the dynamic bonds are the same size as the polymer chain bonds, simulations and theory show an enhancement in the density, because these bonds induce an increased cohesive force in the liquid, which is entropic in origin. The crosslinks are well mixed in the bulk but are depleted from the air and polymer interface. These findings implicate density as a key variable in polymers with dynamic crosslinkers, one that can be used to facilely tune their properties.

cond-mat.soft↗

arXiv2023

Yielding behaviour of active particles in bulk and in confinement

The investigation of collective behaviour in dense assemblies of self-propelled active particles has been motivated by a wide range of biological phenomena. Of particular interest are dynamical transitions of cellular and sub-cellular biological assemblies, including the cytoskeleton and the cell nucleus. Motivated by observations of mechanically induced changes in the dynamics of such systems, and the apparent role of confinement geometry, we study the transition between jammed and fluidized states of active particles assemblies, as a function of the strength and temporal persistence of active forces, and in different confinement geometries. Our results show that the fluidization transition broadly resembles yielding in amorphous solids, consistently with recent suggestions. More specifically, however, we find that a detailed analogy holds with the yielding transition under cyclic shear deformation, for finite persistence times. The fluidization transition is accompanied by driving induced annealing, strong dependence on the initial state of the system, a divergence of time scales to reach steady states, and a discontinuous onset of diffusive motion. We also observe a striking dependence of the transition on persistence times and on the nature of the confinement. Collectively, our results have implications in epigenetic cell state transitions induced by alterations in confinement geometry.

cond-mat.soft↗

arXiv2023

StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes

Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics. Here, we present a graph reinforcement learning approach, StriderNET, that learns a policy to displace the atoms towards low energy configurations. We evaluate the performance of StriderNET on three complex atomic systems, namely, binary Lennard-Jones particles, calcium silicate hydrates gel, and disordered silicon. We show that StriderNET outperforms all classical optimization algorithms and enables the discovery of a lower energy minimum. In addition, StriderNET exhibits a higher rate of reaching minima with energies, as confirmed by the average over multiple realizations. Finally, we show that StriderNET exhibits inductivity to unseen system sizes that are an order of magnitude different from the training system.

cs.LG↗