9 research outputs found
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
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.Comment: 8 pages, 4 figure
Roadmap on machine learning glassy liquids
Unraveling the connections between microscopic structure, emergent physical
properties, and slow dynamics has long been a challenge in the field of the
glass transition. The absence of clear visible structural order in amorphous
configurations complicates the identification of the key features related to
structural relaxation and transport properties. The difficulty in sampling
equilibrated configurations at low temperatures hampers thorough numerical and
theoretical investigations. This roadmap 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 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 glassy liquids. To foster a collaborative community effort,
the article introduces the "GlassBench" dataset, providing simulation data and
benchmarks for both two-dimensional and three-dimensional glass-formers.
Emphasizing the importance of benchmarks, we identify critical metrics for
comparing 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
understanding of glassy liquids
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 graph neural networks - 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
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
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 graph neural networks - 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
Point defects in crystals of charged colloids
Charged colloidal particlesâon both the nano and micron scalesâhave 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 interstitialsâwhich can dramatically impact their material propertiesâtheir existence is usually ignored in scientific studies. Here, we use computer simulations and free-energy calculations to characterize vacancies and interstitials in 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 observe these elusive defects in the lab
Roadmap on machine learning glassy liquids
International audienceUnraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge in the field of the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key features related to structural relaxation and transport properties. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This roadmap 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 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 glassy liquids. To foster a collaborative community effort, the article introduces the "GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. Emphasizing the importance of benchmarks, we identify critical metrics for comparing 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 understanding of glassy liquids
Roadmap on machine learning glassy liquids
International audienceUnraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge in the field of the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key features related to structural relaxation and transport properties. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This roadmap 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 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 glassy liquids. To foster a collaborative community effort, the article introduces the "GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. Emphasizing the importance of benchmarks, we identify critical metrics for comparing 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 understanding of glassy liquids