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...

Researchers in the field of materials science, chemistry, and computational physics are regularly posed with the challenge of managing large and heterogeneous data spaces. The amount of data increases in lockstep with computational efficiency multiplied by the amount of available computational resources, which shifts the bottleneck in the scientific process from data acquisition to data processing and analysis. We present a framework designed to aid in the integration of various specialized data formats, tools and workflows. The signac framework provides all basic components required to create a well-defined and thus collectively accessible and searchable data space, simplifying data access and modification through a homogeneous data interface that is largely agnostic to the data source, i.e., computation or experiment. The framework's data model is designed to not require absolute commitment to the presented implementation, simplifying adaption into existing data sets and workflows. This approach not only increases the efficiency with which scientific results can be produced, but also significantly lowers barriers for collaborations requiring shared data access.

There are few methods for the assembly of defined protein oligomers and higher order structures that could serve as novel biomaterials. Using fluorescent proteins as a model system, we have engineered novel oligomerization states by combining oppositely supercharged variants. A well-defined, highly symmetrical 16mer (two stacked, circular octamers) can be formed from alternating charged proteins; higher order structures then form in a hierarchical fashion from this discrete protomer. During SUpercharged PRotein Assembly (SuPrA), electrostatic attraction between oppositely charged variants drives interaction, while shape and patchy physicochemical interactions lead to spatial organization along specific interfaces, ultimately resulting in protein assemblies never before seen in nature.

We introduce a mean-field theoretical framework for generalizing isotropic pair potentials to anisotropic shapes. This method is suitable for generating pair potentials that can be used in both Monte Carlo and molecular dynamics simulations. We demonstrate the application of this theory by deriving a Lennard-Jones (LJ)-like potential for arbitrary geometries along with a Weeks–Chandler–Anderson-like repulsive variant, showing that the resulting potentials behave very similarly to standard LJ potentials while also providing a nearly conformal mapping of the underlying shape. We then describe an implementation of this potential in the simulation engine HOOMD-blue and discuss the challenges that must be overcome to achieve a sufficiently robust and performant implementation. The resulting potential can be applied to smooth geometries like ellipsoids and to convex polytopes. We contextualize these applications with reference to the existing methods for simulating such particles. The pair potential is validated using standard criteria, and its performance is compared to existing methods for comparable simulations. Finally, we show the results of self-assembly simulations, demonstrating that this method can be used to study the assembly of anisotropic particles into crystal structures.

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.