The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The 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. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods such as the potential of mean force and torque and local crystal environment analysis with equal ease. While many comparable tools place a heavy emphasis on reading and operating on trajectory file formats, freud instead accepts numerical arrays of data directly as inputs. By remaining agnostic to its data source, freud is suitable for analyzing any coarse-grained particle simulation, regardless of the original data representation or simulation method. When used for on-the-fly analysis in conjunction with scriptable simulation software such as HOOMD-blue [1, 2], freud enables smart simulations that adapt to the current state of the system, allowing users to study phenomena such as nucleation and growth. PROGRAM SUMMARYProgram Title: freud Licensing provisions: BSD 3-Clause Programming language: Python, C++ Nature of problem: Simulations of coarse-grained, nano-scale, and colloidal particle systems typically require analyses specialized to a particular system. Certain more standardized techniques -including correlation functions, order parameters, and clustering -are computationally intensive tasks that must be carefully implemented to scale to the larger systems common in modern simulations. Solution method: freud performs a wide variety of particle system analyses, offering a Python API that interfaces with many other tools in computational molecular sciences via NumPy array inputs and outputs. The algorithms in freud leverage parallelized C++ to scale to large systems and enable real-time analysis. The library's broad set of features encode few assumptions compared to other analysis packages, enabling analysis of a broader class of data ranging from biomolecular simulations to colloidal experiments. Unusual features:1. freud provides very fast, parallel implementations of standard analysis methods like RDFs and correlation functions. 2. freud includes the reference implementation for the potential of mean force and torque (PMFT). 3. freud provides various novel methods for characterizing particle environments, including the calculation of descriptors useful for machine learning. Additional comments:The source code is hosted on GitHub (https://github. com/glotzerlab/freud), and documentation is available online (https: //freud.readthedocs.io/). The package may be installed via pip install freud-analysis or conda install -c conda-forge freud. 13 for i, p in enumerate(points): 14 for j in nl.index_j[nl.index_i == i]: 15 for k in...

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.

The likelihood that an undercooled liquid vitrifies or crystallizes depends on the cooling rate R. The critical cooling rate Rc, 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 Rc > 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 Rc) than binary alloys. However, there is currently no theoretical framework that provides quantitative predictions for Rc 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 Rc. 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 σB/σA and σC /σA and number fractions xA, xB, and xC. 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 σC/σA 0.8 is below the demixing threshold for binary systems, adding a third component B with σC < σB < σA 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 σC /σA < 0.8.

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 overhead.

We study the spatio-temporal evolution of the viscosity field during stable and unstable radial flows of glycerol-water solutions in a horizontal Hele-Shaw cell where a localized temperature gradient is imposed. The viscosity field is reconstructed from the measurement of the fluorescence emitted by a viscosity-sensitive molecular probe (Auramine O). For an immiscible flow, the viscosity and temperature fields are obtained accurately. For miscible displacements, we show how the interplay between the viscosity changes of both fluids and the variation of the fluid thickness in the gap prevents obtaining strict quantitative reconstruction of the viscosity field. We explain how the reconstructed viscosity field can nevertheless be interpreted to obtain information about the fluid thickness and the local viscosity and temperature.

The freud Python library analyzes particle data output from molecular dynamics simulations. The library's design and its variety of highperformance methods make it a powerful tool for many modern applications. In particular, freud can be used as part of the data generation pipeline for machine learning (ML) algorithms for analyzing particle simulations, and it can be easily integrated with various simulation visualization tools for simultaneous visualization and real-time analysis. Here, we present numerous examples both of using freud to analyze nano-scale particle systems by coupling traditional simulational analyses to machine learning libraries and of visualizing per-particle quantities calculated by freud analysis methods. We include code and examples of this visualization, showing that in general the introduction of freud into existing ML and visualization workflows is smooth and unintrusive. We demonstrate that among Python packages used in the computational molecular sciences, freud offers a unique set of analysis methods with efficient computations and seamless coupling into powerful data analysis pipelines.

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