arXiv subjects

Rinske M. Alkemade

Publications and source records attributed to Rinske M. Alkemade.

8 recordsLinked to original sources

arXiv2026

Auxetic behaviour in crystals of hard polyhedra

Auxetic materials - systems that, when subjected to a compression in one direction, also compress in one or more perpendicular directions - have intrigued researchers for decades due to their counterintuitive mechanical properties. Their unique behaviour gives auxetic materials potential for a wide range of applications such as shock absorbers, and electrodes in piezoelectric sensors. Most known auxetic materials are realized by connecting rigid, anisotropic units in a hinging manner, which are systems that often are hard to realize experimentally on the microscopic scale. Here, we explore the elastic behaviour of six crystals composed of discrete space-filling hard polygons or polyhedra. We show that some of these systems show partial auxetic behaviour, emerging from the interplay of entropy and geometry alone. To demonstrate the feasibility and robustness of this phenomenon, we create two experimental realizations of square-shaped particles, spanning both colloidal particles driven by Brownian motion and granular particles driven by external vibrations, and confirm the emergence of auxeticity in both cases.

cond-mat.soft↗

arXiv2026

Determining fluid-crystal phase boundaries for a binary hard-sphere mixture using direct-coexistence simulations

Determining fluid-crystal phase boundaries via direct-coexistence methods can be challenging due to the fact that the simulation box can introduce crystal strain. Recently, a direct-coexistence approach was developed which allows one to easily identify the equilibrium strain-free fluid-crystal coexistence in monodisperse systems. Here, we show that this approach can be readily extended to binary mixtures forming stoichiometric binary crystals, allowing accurate and efficient determination of the phase boundaries. Moreover, we examine how the choice of crystal plane in contact with the fluid affects the accuracy of the phase boundary determination. The method is easy to implement and does not require prior knowledge of the binary fluid's equation of state. These results further establish the method as a robust and practical tool for accurately determining fluid-crystal phase boundaries.

cond-mat.soft↗

arXiv2026

Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells

Machine learning (ML) strategies are opening the door to faster computer simulations, allowing us to simulate more realistic colloidal systems. Since the interactions in colloidal systems are often highly many-body, stemming from e.g. depletion and steric interactions, one of the challenges for these algorithms is capturing the many-body nature of these interactions. In this paper, we introduce a new ML-based strategy for fitting many-body interactions in colloidal systems where the many-body interaction is highly local. To this end, we develop Voronoi-based descriptors for capturing the local environment and fit the effective potential using a simple neural network. To test this algorithm, we consider a simple two-dimensional model for a colloid-polymer mixture, where the colloid-colloid interactions and colloid-polymer interactions are hard-disk like, while the polymers themselves interact as ideal gas particles. We find that a Voronoi-based description is sufficient to accurately capture the many-body nature of this system. Moreover, we find that the Pearson correlation function alone is insufficient to determine the predictive power of the network emphasizing the importance of additional metrics when assessing the quality of ML-based potentials.

cond-mat.soft↗

arXiv2026

Characterizing the cage state of glassy systems and its sensitivity to frozen boundaries

Understanding the role that structure plays in the dynamical arrest observed in glassy systems remains an open challenge. Over the last decade, machine learning (ML) strategies have emerged as an important tool for probing this structure-dynamics relationship, particularly for predicting heterogeneous glassy dynamics from local structure. A recent advancement is the introduction of the cage state, a structural quantity that captures the average positions of particles while rearrangements are forbidden. During the caging regime, linear models trained on the cage state have been shown to outperform more complex ML methods trained on initial configurations only. In this paper, we explore the properties associated with the cage state in more detail to better understand why it serves as such an effective predictor for the dynamics. Specifically, we examine how the cage state in a binary hard-sphere mixture is influenced by both packing fraction and boundary conditions. Our results reveal that, as the system approaches the glassy regime, the cage state becomes increasingly influenced by long-range structural effects. This influence is evident both in its predictive power for particle dynamics and in the internal structure of the cage state, suggesting that the CS might be associated with some form of an amorphous growing structural length scale.

cond-mat.soft↗

arXiv2024

Roadmap on machine learning glassy dynamics

Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key physical mechanisms underpinning slow dynamics. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This perspective article explores the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present recent successful ML applications, as well as many open problems for the future, such as transferability and interpretability of ML approaches. We highlight new ideas and directions in which ML could provide breakthroughs to better understand the fundamental mechanisms at play in glass-forming liquids. To foster a collaborative community effort, this article also introduces the ``GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. We propose critical metrics to compare the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. The goal of this roadmap is to provide guidelines for the development of ML techniques in systems displaying slow dynamics, while inspiring new directions to improve our theoretical understanding of glassy liquids.

cond-mat.soft↗

arXiv2023

Improving the prediction of glassy dynamics by pinpointing the local cage

The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To do this we introduce a method to pinpoint the cage state of the initial configuration -- i.e. the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime.

cond-mat.soft↗

arXiv2022

Comparing machine learning techniques for predicting glassy dynamics

In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learning techniques, and increasingly sophisticated descriptors used to describe the environment around particles. In many cases, both the chosen machine learning technique and choice of structural descriptors are varied simultaneously, making it hard to quantitatively compare the performance of different machine learning approaches. Here, we use three different machine learning algorithms -- linear regression, neural networks, and GNNs -- to predict the dynamic propensity of a glassy binary hard-sphere mixture using as structural input a recursive set of order parameters recently introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we show, when these advanced descriptors are used, all three methods predict the dynamics with nearly equal accuracy. However, the linear regression is orders of magnitude faster to train making it by far the method of choice.

cond-mat.soft↗

arXiv2021

Point Defects in Crystals of Charged Colloids

Charged colloidal particles - both on the nano and micron scales - have been instrumental in enhancing our understanding of both atomic and colloidal crystals. These systems can be straightforwardly realized in the lab, and tuned to self-assemble into body-centered cubic (BCC) and face-centered cubic (FCC) crystals. While these crystals will always exhibit a finite number of point defects, including vacancies and interstitials - which can dramatically impact their material properties - their existence is usually ignored in scientific studies. Here, we use computer simulations and free-energy calculations to characterize vacancies and interstitials in both FCC and BCC crystals of point-Yukawa particles. We show that, in the BCC phase, defects are surprisingly more common than in the FCC phase, and the interstitials manifest as so-called crowdions: an exotic one-dimensional defect proposed to exist in atomic BCC crystals. Our results open the door to directly observing these elusive defects in the lab.

cond-mat.soft↗