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Bradley D. Dice

Bio: Bradley D. Dice is an academic researcher from University of Michigan. The author has contributed to research in topics: Python (programming language) & Viscosity. The author has an hindex of 4, co-authored 9 publications receiving 93 citations. Previous affiliations of Bradley D. Dice include William Jewell College & Yale University.

Papers
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Journal ArticleDOI
TL;DR: The freud Python package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools, enabling analysis of a broader class of data ranging from biomolecular simulations to colloidal experiments.

114 citations

Journal ArticleDOI
TL;DR: More than 150,000 photonic band calculations for thousands of natural crystal templates from which they predict 351 photonic crystal templates - including nearly 300 previously-unreported structures - that can potentially be realized for a multitude of applications and length scales, including several in the visible range via colloidal self-assembly as discussed by the authors.
Abstract: Many butterflies, birds, beetles, and chameleons owe their spectacular colors to the microscopic patterns within their wings, feathers, or skin. When these patterns, or photonic crystals, result in the omnidirectional reflection of commensurate wavelengths of light, it is due to a complete photonic band gap (PBG). The number of natural crystal structures known to have a PBG is relatively small, and those within the even smaller subset of notoriety, including diamond and inverse opal, have proven difficult to synthesize. Here, we report more than 150,000 photonic band calculations for thousands of natural crystal templates from which we predict 351 photonic crystal templates - including nearly 300 previously-unreported structures - that can potentially be realized for a multitude of applications and length scales, including several in the visible range via colloidal self-assembly. With this large variety of 3D photonic crystals, we also revisit and discuss oft-used primary design heuristics for PBG materials.

42 citations

Journal ArticleDOI
TL;DR: In this paper, the authors studied the role of geometric frustration and demixing in determining the critical cooling rate R(c) for ternary hard-sphere systems, and found that when the diameter ratios are close to 1, such that the largest (A) and smallest (C) species are well-mixed, the glass-forming ability of such systems is no better than that of the optimal binary glass former.
Abstract: The likelihood that an undercooled liquid vitrifies or crystallizes depends on the cooling rate R. The critical cooling rate R(c), below which the liquid crystallizes upon cooling, characterizes the glass-forming ability (GFA) of the system. While pure metals are typically poor glass formers with R(c)>10(12)K/s, specific multi-component alloys can form bulk metallic glasses (BMGs) even at cooling rates below R∼1 K/s. Conventional wisdom asserts that metal alloys with three or more components are better glass formers (with smaller R(c)) than binary alloys. However, there is currently no theoretical framework that provides quantitative predictions for R(c) for multi-component alloys. In this manuscript, we perform simulations of ternary hard-sphere systems, which have been shown to be accurate models for the glass-forming ability of BMGs, to understand the roles of geometric frustration and demixing in determining R(c). Specifically, we compress ternary hard sphere mixtures into jammed packings and measure the critical compression rate, below which the system crystallizes, as a function of the diameter ratios σ(B)/σ(A) and σ(C)/σ(A) and number fractions x(A), x(B), and x(C). We find two distinct regimes for the GFA in parameter space for ternary hard spheres. When the diameter ratios are close to 1, such that the largest (A) and smallest (C) species are well-mixed, the GFA of ternary systems is no better than that of the optimal binary glass former. However, when σ(C)/σ(A) ≲ 0.8 is below the demixing threshold for binary systems, adding a third component B with σ(C) < σ(B) < σ(A) increases the GFA of the system by preventing demixing of A and C. Analysis of the available data from experimental studies indicates that most ternary BMGs are below the binary demixing threshold with σ(C)/σ(A) < 0.8.

31 citations

Proceedings ArticleDOI
01 Jan 2018
TL;DR: This talk showcases signac, an open-source Python framework that offers highly modular and scalable solutions for versatile data and workflow management tools that can easily adapt to the highly dynamic requirements of scientific investigations.
Abstract: Computational research requires versatile data and workflow management tools that can easily adapt to the highly dynamic requirements of scientific investigations. Many existing tools require strict adherence to a particular usage pattern, so researchers often use less robust ad hoc solutions that they find easier to adopt. The resulting data fragmentation and methodological incompatibilities significantly impede research. Our talk showcases signac, an open-source Python framework that offers highly modular and scalable solutions for this problem. Named for the Pointillist painter Paul Signac, the framework’s powerful workflow management tools enable users to construct and automate workflows that transition seamlessly from laptops to HPC clusters. Crucially, the underlying data model is completely independent of the workflow. The flexible, serverless, and schema-free signac database can be introduced into other workflows with essentially no overhead and no recourse to the signac workflow model. Additionally, the data model’s simplicity makes it easy to parse the underlying data without using signac at all. This modularity and simplicity eliminates significant barriers for consistent data management across projects, facilitating improved provenance management and data sharing with minimal

13 citations

Journal ArticleDOI
TL;DR: Simulation of ternary hard-sphere systems, which have been shown to be accurate models for the glass-forming ability of BMGs, are performed to understand the roles of geometric frustration and demixing in determining R(c), and two distinct regimes for the GFA in parameter space are found.
Abstract: The critical cooling rate $\mathcal{R}_c$, below which liquids crystallize upon cooling, characterizes the glass-forming ability (GFA) of the system. While pure metals are typically poor glass formers with $\mathcal {R}_c>10^{12}\, {\rm K/s}$, specific multi-component alloys can form bulk metallic glasses (BMGs) even at cooling rates below $\mathcal {R}\sim 1\, {\rm K/s}$. Conventional wisdom asserts that metal alloys with three or more components are better glass formers (with smaller ${\cal R}_c$) than binary alloys. However, there is currently no theoretical framework that provides quantitative predictions for $\mathcal{R}_c$ for multi-component alloys. We perform simulations of ternary hard-sphere systems, which have been shown to be accurate models for the glass-forming ability of BMGs, to understand the roles of geometric frustration and demixing in determining $\mathcal {R}_c$. Specifically, we compress ternary hard sphere mixtures into jammed packings and measure the critical compression rate, below which the system crystallizes, as a function of the diameter ratios $\sigma_B/\sigma_A$ and $\sigma_C/\sigma_A$ and number fractions $x_A$, $x_B$, and $x_C$. We find two distinct regimes for the GFA in parameter space for ternary hard spheres. When the diameter ratios are close to $1$, such that the largest ($A$) and smallest ($C$) species are well-mixed, the GFA of ternary systems is no better than that of the optimal binary glass former. However, when $\sigma_C/\sigma_A \lesssim 0.8$ is below the demixing threshold for binary systems, adding a third component $B$ with $\sigma_C < \sigma_B < \sigma_A$ increases the GFA of the system by preventing demixing of $A$ and $C$. Analysis of the available data from experimental studies indicates that most ternary BMGs are below the binary demixing threshold with $\sigma_C/\sigma_A < 0.8$.

10 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
TL;DR: This work trains a machine learning model on previously reported observations, parameters from physiochemical theories, and makes it synthesis method–dependent to guide high-throughput experiments to find a new system of metallic glasses in the Co-V-Zr ternary, and provides a quantitatively accurate, synthesis method-sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses.
Abstract: With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.

355 citations

Journal ArticleDOI
TL;DR: In this paper, a comprehensive analysis of eleven glass-forming models is presented to demonstrate that both structural ordering and the dramatic increase of relaxation times at low temperatures can be efficiently tackled using carefully designed models of size polydisperse supercooled liquids together with an efficient Monte Carlo algorithm where translational particle displacements are complemented by swaps of particle pairs.
Abstract: Successful computer studies of glass-forming materials need to overcome both the natural tendency to structural ordering and the dramatic increase of relaxation times at low temperatures. We present a comprehensive analysis of eleven glass-forming models to demonstrate that both challenges can be efficiently tackled using carefully designed models of size polydisperse supercooled liquids together with an efficient Monte Carlo algorithm where translational particle displacements are complemented by swaps of particle pairs. We study a broad range of size polydispersities, using both discrete and continuous mixtures, and we systematically investigate the role of particle softness, attractivity and non-additivity of the interactions. Each system is characterized by its robustness against structural ordering and by the efficiency of the swap Monte Carlo algorithm. We show that the combined optimisation of the potential's softness, polydispersity and non-additivity leads to novel computer models with excellent glass-forming ability. For such models, we achieve over ten orders of magnitude gain in the equilibration timescale using the swap Monte Carlo algorithm, thus paving the way to computational studies of static and thermodynamic properties under experimental conditions. In addition, we provide microscopic insights into the performance of the swap algorithm which should help optimizing models and algorithms even further.

146 citations

Journal ArticleDOI
TL;DR: By applying the support vector classification method, models for predicting the GFA of binary metallic alloys from random compositions are developed and suggest that machine learning is very powerful and efficient and has great potential for discovering new metallic glasses with good GFA.
Abstract: The prediction of the glass-forming ability (GFA) by varying the composition of alloys is a challenging problem in glass physics, as well as a problem for industry, with enormous financial ramifications. Although different empirical guides for the prediction of GFA were established over decades, a comprehensive model or approach that is able to deal with as many variables as possible simultaneously for efficiently predicting good glass formers is still highly desirable. Here, by applying the support vector classification method, we develop models for predicting the GFA of binary metallic alloys from random compositions. The effect of different input descriptors on GFA were evaluated, and the best prediction model was selected, which shows that the information related to liquidus temperatures plays a key role in the GFA of alloys. On the basis of this model, good glass formers can be predicted with high efficiency. The prediction efficiency can be further enhanced by improving larger database and refined i...

131 citations

Journal ArticleDOI
TL;DR: A descriptor based on the heuristics that structural and energetic ‘confusion' obstructs crystalline growth is proposed, and its validity is demonstrated by experiments on two well-known glass-forming alloy systems.
Abstract: Metallic glasses attract considerable interest due to their unique combination of superb properties and processability. Predicting their formation from known alloy parameters remains the major hindrance to the discovery of new systems. Here, we propose a descriptor based on the heuristics that structural and energetic 'confusion' obstructs crystalline growth, and demonstrate its validity by experiments on two well-known glass-forming alloy systems. We then develop a robust model for predicting glass formation ability based on the geometrical and energetic features of crystalline phases calculated ab initio in the AFLOW framework. Our findings indicate that the formation of metallic glass phases could be much more common than currently thought, with more than 17% of binary alloy systems potential glass formers. Our approach pinpoints favourable compositions and demonstrates that smart descriptors, based solely on alloy properties available in online repositories, offer the sought-after key for accelerated discovery of metallic glasses.

116 citations