arXiv subjects

Anshul D. S. Parmar

Publications and source records attributed to Anshul D. S. Parmar.

At least 19 recordsLinked to original sources

arXiv2026

Transmutation-accelerated sampling method for multi-component ZrCu(Al) metallic glasses

We investigate multi-component metallic glass systems using a hybrid Molecular Dynamics (MD) and Variance-Constrained Semi-Grand Canonical approach. This method enables us to generate samples with properties consistent with experimental observations, at deeply supercooled states that are typically inaccessible with conventional MD simulations. Using a realistic interatomic potential, we investigate the dynamics, kinetic stability, and rheology of a ZrCu(Al) metallic glass, together with the widely studied ZrCu system, in the low-temperature glassy regime. Our results demonstrate how the hybrid method enhances relaxation and provides a generic framework for modeling realistic complex metallic glasses.

cond-mat.dis-nn↗

arXiv2026

Starting from the amorphous ground state: linking landscape thermodynamics to slow dynamics and crossover

A microscopic understanding of low-temperature thermodynamics and its relation to dynamical features such as a fragile-to-strong crossover (FSC) remains a central challenge in glass physics. Using swap Monte Carlo combined with a full potential-energy-landscape (PEL) analysis of a non-network-forming model, we obtain equilibrium data deep into the glassy regime and identify a finite system size that simultaneously reproduces bulk behaviour for $T \gtrsim T_g/2$ and allows complete sampling of the PEL down to its lowest-energy amorphous states. This enables the direct computation of the configurational entropy over the full temperature range of the finite system without relying on liquid-state thermodynamic integration. We find a pronounced depletion of low-energy states relative to the Gaussian regime of the PEL, which governs the low-temperature curvature of the configurational entropy. Numerically, the apparent activation energy of the diffusivity closely follows the temperature dependence of the mean inherent structure energy and exhibits a gradual crossover towards Arrhenius-like behaviour. This correlation is consistent with a trap-model description of the PEL, in which the FSC emerges naturally as a consequence of the depletion of low-energy states and thus of the lower bound of the PEL. We further argue, as illustrated analytically for a simple binomial model of the PEL, that the observability of a FSC depends on whether the depletion regime is reached within the accessible temperature window.

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

Fracture toughness and auxeticity in disordered metamaterials

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

cond-mat.dis-nn↗

arXiv2025

Efficient training of machine learning potentials for metallic glasses: CuZrAl validation

Interatomic potentials are key to uncovering microscopic structure-property relationships, essential for multiscale simulations and high-throughput experiments. For metallic glasses, their disordered atomic structure makes the development of potentials particularly challenging, resulting in the scarcity of chemistry-specific parametrizations for this important class of materials. We address this gap by introducing an efficient methodology to design machine learning interatomic potentials (MLIPs), benchmarked on the CuZrAl system. Using a Lennard-Jones surrogate model, swap-Monte Carlo sampling, and single-point Density Functional Theory (DFT) corrections, we capture amorphous structures spanning 14 decades of supercooling. These representative configurations, competing with the experimental time scale, enable robust model training across diverse states, while minimizing the need for extensive DFT datasets. The resulting MLIP matches the experimental data and predictions of the classical embedded atom method (EAM) for structural, dynamical, energetic, and mechanical properties. This approach offers a scalable path to develop accurate MLIPs for complex metallic glasses, including emerging multi-component and high-entropy systems.

cond-mat.mtrl-sci↗

arXiv2025

Growth and prediction of plastic strain in metallic glasses

Predicting the failure and plasticity of solids remains a longstanding challenge, with broad implications for materials design and functional reliability. Disordered solids like metallic glasses can fail either abruptly or gradually without clear precursors, and the mechanical response depends strongly on composition, thermal history and deformation protocol -- impeding generalizable modeling. While deep learning methods offer predictive power, they often rely on numerous input parameters, hindering interpretability, methodology advancement and practical deployment. Here, we propose a macroscopic, physically grounded approach that uses plastic strain accumulation in the elastic regime to robustly predict deformation and yield. This method reduces complexity and improves interpretability, offering a practical alternative for disordered materials. For the Cu-Zr-(Al) metallic glasses prepared with varied annealing, we identify two limiting regimes of plastic strain growth: power-law in poorly annealed and exponential in well-annealed samples. A physics-informed framework with Bayesian inference extracts growth parameters from stress-strain data within $\sim$5\% strain, enabling early prediction of bulk response and yield point, well before the failure. The predictive performance improves with annealing, and bulk plasticity correlates with the microscopic plastic activity from scattered to growth near yielding. This work presents a physically interpretable and experimentally relevant framework for predicting plasticity and failure in metallic glasses from early mechanical response, offering both theoretical insights and practical tools for material characterization and design.

cond-mat.mtrl-sci↗

arXiv2025

Exploring the impact of Ti/Al on L12 nanoprecipitation and deformation behavior in CoNiFeAlTi multi-principal element alloys through atomistic simulations

Recent studies on CoNi-based multi-principal element alloys (MPEAs) have demonstrated high strength and ductility, attributed to the formation of stable L12 nanoscale precipitates. However, the fundamental mechanisms behind such impressive properties in these complex alloys are not well understood. In this work, we investigate the effects of Ti and Al concentrations on the formation of L12 precipitates in (CoNiFe)84(Al8Ti8), (CoNiFe)86(Al7Ti7), (CoNiFe)88(Al6Ti6), and (CoNiFe)94(Al4Ti2) MPEAs using hybrid molecular dynamics/Monte Carlo (MD/MC) simulations and a MEAM interatomic potential for the CoNiFeTiAl system. Additionally, we study the effect of L12 precipitation on the mechanical properties and stacking fault energy (SFE) of these MPEAs using MD. Our hybrid MD/MC simulations show that the (CoNiFe)86(Al7Ti7) alloy exhibits the highest amount of L12 nanoprecipitates. We find that L12 precipitation increases the SFE, with higher Al and Ti contents leading to greater increases. Tensile simulations reveal that L12 precipitates enhance yield strength, with alloys exhibiting higher precipitation showing increased flow stress. We also investigate dislocation-nanoprecipitate interactions with different precipitate sizes in the (CoNiFe)86(Al7Ti7) alloy. Larger nanoprecipitate sizes result in stronger dislocation pinning. Dislocations predominantly shear through 4-8 nm precipitates instead of looping around them (Orowan mechanism), enhancing strength while maintaining good ductility. Although the lattice mismatch between the L12 nanoprecipitate and the matrix is low (0.139%), the significant difference in SFE between the L12 nanoprecipitate and the matrix results in stronger dislocation pinning. This understanding can guide the design of MPEAs with tailored properties by controlling nanoscale precipitation.

cond-mat.mtrl-sci↗

arXiv2024

Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties

Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore. Here, we tackle this challenge by finding optimal compositions for target mechanical properties. We apply Bayesian exploration for the CuZrAl composition, a paradigmatic metallic glass known for its good glass forming ability. We exploit an automated loop with an online database, a Bayesian optimization algorithm, and molecular dynamics simulations. From the ubiquitous 50/50 CuZr starting point, we map the composition landscape changing the ratio of elements and adding aluminium to characterize the yield stress and the shear modulus. This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminium concentration $c_{\rm Al} = 15$ \% and zirconium concentration $c_{\rm Zr} = 30$ %. We also explore several cooling rates ("process parameters") and find that the best mechanical properties for a composition result from being most affected by the cooling procedure. Our Bayesian approach paves the novel way for the design of metallic glasses with "small data", with an eye toward both future in silico design and experimental applications exploiting this toolbox.

cond-mat.mtrl-sci↗

arXiv2024

Avalanches in CuZrAl metallic glasses

Metallic glasses have mechanical properties, which exhibit avalanches in the disguise of stress drops. We study these phenomena in a classical metallic glass system CuZrAl by athermal quasistatic shear and varying the element concentrations and for pure CuZr 50/50 case the cooling rate. The resulting mechanical properties are close to the behaviour found experimentally. At small strains, the pristine systems are akin to other glassy systems with a so-called gap distribution with a small positive exponent. Critical avalanching behaviour is found only approaching the yield point. The post-yield stress drops are universal, and the gap distribution becomes flat.

cond-mat.stat-mech↗

arXiv2022

Annealing glasses by cyclic shear deformation

A major challenge in simulating glassy systems is the ability to generate configurations that may be found in equilibrium at sufficiently low temperatures, in order to probe static and dynamic behaviour close to the glass transition. A variety of approaches have recently explored ways of surmounting this obstacle. Here, we explore the possibility of employing mechanical agitation, in the form of cyclic shear deformation, to generate low energy configurations in a model glass former. We perform shear deformation simulations over a range of temperatures, shear rates and strain amplitudes. We find that shear deformation induces faster relaxation towards low energy configurations, or overaging, in simulations at sufficiently low temperatures, consistently with previous results for athermal shear. However, for temperatures at which simulations can be run till a steady state is reached with or without shear deformation, we find that the inclusion of shear deformation does not result in any speed up of the relaxation towards low energy configurations. Although we find the configurations from shear simulations to have properties indistinguishable from an equilibrium ensemble, the cyclic shear procedure does not guarantee that we generate an equilibrium ensemble at a desired temperature. In order to ensure equilibrium sampling, we develop a hybrid Monte Carlo algorithm that employs cyclic shear as a trial generation step, and has acceptance probabilities that depend not only on the change in internal energy but also on heat dissipated (equivalently, work done). We show that such an algorithm indeed generates an equilibrium ensemble.

cond-mat.soft↗

arXiv2021

Mechanical Behaviour of Glasses and Amorphous Materials

A wide range of materials can exist in microscopically disordered solid forms, referred to as amorphous solids or glasses. Such materials -- oxide glasses and metallic glasses, to polymer glasses, and soft solids such as colloidal glasses, emulsions and granular packings -- are useful as structural materials in a variety of contexts. Their deformation and flow behaviour is relevant for many others. Apart from fundamental questions associated with the formation of these solids, comprehending their mechanical behaviour is thus of interest, and of significance for their use as materials. In particular, the nature of plasticity and yielding behaviour in amorphous solids has been actively investigated. Different amorphous solids exhibit behaviour that is apparently diverse and qualitatively different from those of crystalline materials. A goal of recent investigations has been to comprehend the unifying characteristics of amorphous plasticity and to understand the apparent differences among them. We summarise some of the recent progress in this direction. We focus on insights obtained from computer simulation studies, and in particular those employing oscillatory shear deformation of model glasses.

cond-mat.soft↗

arXiv2021

Hidden Order Beyond Hyperuniformity in Critical Absorbing States

Disordered hyperuniformity is a description of hidden correlations in point distributions revealed by an anomalous suppression in fluctuations of local density at various coarse-graining length scales. In the absorbing phase of models exhibiting an active-absorbing state transition, this suppression extends up to a hyperuniform length scale that diverges at the critical point. Here, we demonstrate the existence of additional many-body correlations beyond hyperuniformity. These correlations are hidden in the higher moments of the probability distribution of the local density, and extend up to a longer length scale with a faster divergence than the hyperuniform length on approaching the critical point. Our results suggest that a hidden order beyond hyperuniformity may generically be present in complex disordered systems.

cond-mat.stat-mech↗

arXiv2020

Ultrastable metallic glasses in silico

We develop a generic strategy and simple numerical models for multi-component metallic glasses for which the swap Monte Carlo algorithm can produce highly stable equilibrium configurations equivalent to experimental systems cooled more than $10^7$ times slower than in conventional simulations. This paves the way for a deeper understanding of thermodynamic, dynamic, and mechanical properties of metallic glasses. As first applications, we considerably extend configurational entropy measurements down to the experimental glass temperature, and demonstrate a qualitative change of the mechanical response of metallic glasses of increasing stability towards brittleness.

cond-mat.stat-mech↗

arXiv2020

Stable glassy configurations of the Kob-Andersen model using swap Monte Carlo

The swap Monte Carlo algorithm allows the preparation of highly stable glassy configurations for a number of glass-formers, but is inefficient for some models, such as the much studied binary Kob-Andersen (KA) mixture. We have recently developed generalisations to the KA model where swap can be very effective. Here, we show that these models can in turn be used to considerably enhance the stability of glassy configurations in the original KA model at no computational cost. We successfully develop several numerical strategies both in and out of equilibrium to achieve this goal and show how to optimise them. We provide several physical measurements indicating that the proposed algorithms considerably enhance mechanical and thermodynamic stability in the KA model, including a transition towards brittle yielding behaviour. Our results thus pave the way for future studies of stable glasses using the KA model.

cond-mat.stat-mech↗

arXiv2020

Hyperuniformity in cyclically driven glasses

We present a numerical investigation of the density fluctuations in a model glass under cyclic shear deformation. At low amplitude of shear, below yielding, the system reaches a steady absorbing state in which density fluctuations are suppressed revealing a clear fingerprint of hyperuniformity up to a finite length scale. The opposite scenario is observed above yielding, where the density fluctuations are strongly enhanced. We demonstrate that the transition to this state is accompanied by a spatial phase separation into two distinct hyperuniform regions, as a consequence of shear band formation above the yield amplitude.

cond-mat.dis-nn↗

arXiv2018

Strain localisation above the yielding point in cyclically deformed glasses

We study the yielding behaviour of a model glass under cyclic athermal quastistatic deformation computationally, and show that yielding is characterised by the discontinuous appearance of shear bands, whose width is about ten particle diameters at their initiation, in which the strain gets localised. Strain localisation is accompanied by a corresponding change in the energies, and a decrease in the density in the shear band. We show that the glass remains well annealed outside the shear band whereas the energies correspond to the highest possible energy minima at the given density within the shear band. Diffusive motion of particles characterising the yielded state are confined to the shear bands, whose mean positions display movement over repeated cycles. Outside the shear band, particle motions are sub-diffusive but remain finite. Despite the discontinuous nature of their appearance, shear bands are reversible in the sense that a reduction in the amplitude of cyclic deformation to values below yielding leads to the healing and disappearance of the shear bands.

cond-mat.dis-nn↗

arXiv2018

Power law relationship between diffusion coefficients in multi-component glass forming liquids

The slow down of dynamics in glass forming liquids as the glass transition is approached has been characterised through the Adam-Gibbs relation, which relates relaxation time scales to the configurational entropy. The Adam-Gibbs relation cannot apply simultaneously to all relaxation times scales unless they are coupled, and exhibit closely related temperature dependences. The breakdown of the Stokes-Einstein relation presents an interesting situation to the contrary, and in analysing it, it has recently been shown that the Adam-Gibbs relation applies to diffusion coefficients rather than to viscosity or structural relaxation times related to the decay of density fluctuations. However, for multi-component liquids -- the typical cases considered in computer simulations, metallic glass formers, etc. -- such a statement raises the question of which diffusion coefficient is described by the Adam-Gibbs relation. All diffusion coefficients can be consistently described by the Adam-Gibbs relation if they bear a power law relationship with each other. Remarkably, we find that for a wide range of glass formers, and for a wide range of temperatures spanning the normal and the slow relaxation regimes, such a relationship holds. We briefly discuss possible rationalisations of the observed behaviour.

cond-mat.soft↗

arXiv2017

Density dependence of relaxation dynamics in glass formers, and the dependence of their fragility on the softness of inter-particle interactions

Fragility, quantifying the rapidity of variation of relaxation times, is analysed for a series of model glass formers, which differ in the softness of their interparticle interactions. In an attempt to rationalize experimental observations in colloidal suspensions that softer interactions lead to stronger (less fragile) glass formers, we study the variation of relaxation dynamics with density, rather than temperature, as a control parameter. We employ density temperature scaling, analyzed in recent studies, to address the question. We find that while employing inverse density in place of temperature leads to the conclusion that softer interactions lead to stronger behaviour, the use of scaled variables involving temperature and density lead to the opposite conclusion, similarly to earlier investigations where temperature variation of relaxation dynamics was analysed for the same systems. We rationalize our results by considering the Adam-Gibbs (AG) fragility, which incorporates the density dependence of the configurational entropy and an activation energy that may arise from other properties of a glass former. Within the framework of the Adam-Gibbs relation, by employing density temperature scaling for the analysis, we find that softer particles make more fragile glasses, as deduced from dynamical quantities, which is found to be consistent with the Adam-Gibbs fragility.

cond-mat.soft↗