Machine learning for crystal identification and discovery

Spellings, Matthew; Glotzer, Sharon C.

doi:10.1002/aic.16157

Cited by 92 publications

(86 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was highlighted in a recent workshop organized by The National Academy of Sciences . A quick review of recent progress shows a variety of applications such as the design of crystalline alloys and organic photovoltaics, nanoparticle packing and assembly, estimates of physical properties of small organic molecules, design of shape memory alloys, determination of local structural features of ferritic and austenitic phases of bulk iron, colloidal self‐assembly, simplifying model development in transition metal chemistry …”

Section: Ai In Chemical Engineering: Recent Trends and Future Outlookmentioning

confidence: 99%

The promise of artificial intelligence in chemical engineering: Is it here, finally?

2018

View full text Add to dashboard Cite

Section: Ai In Chemical Engineering: Recent Trends and Future Outlookmentioning

confidence: 99%

The promise of artificial intelligence in chemical engineering: Is it here, finally?

2018

View full text Add to dashboard Cite

“…For example, distinguishing different grains of the same crystal structure could be done using descriptors that are not rotationally invariant. Alternatively, we can often obtain rotationallyinvariant descriptions of local environments for crystal structure identification via the principal axes of the moment of inertia tensor of the environments, or by using particle orientations of anisotropic particles [19,38,48]. To support such spherical harmonic analyses, the LocalDescriptors class in freud computes spherical harmonics characterizing particle neighborhoods.…”

Section: Spherical Harmonic Descriptorsmentioning

confidence: 99%

freud: A software suite for high throughput analysis of particle simulation data

Ramasubramani

Dice

Harper

et al. 2020

Computer Physics Communications

Self Cite

197

134

View full text Add to dashboard Cite

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...

show abstract

“…A wide range of problems in soft matter and nano-scale simulations have been addressed using machine learning techniques, such as crystal structure identification [SG18]. In machine learning workflows, freud is used to generate features, which are then used in classification or regression models, clusterings, or dimensionality reduction methods.…”

Section: Machine Learningmentioning

confidence: 99%

Analyzing Particle Systems for Machine Learning and Data Visualization with freud

Dice¹,

Ramasubramani²,

Harper³

et al. 2019

Proceedings of the Python in Science Conference

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Machine learning for crystal identification and discovery

Cited by 92 publications

References 40 publications

The promise of artificial intelligence in chemical engineering: Is it here, finally?

The promise of artificial intelligence in chemical engineering: Is it here, finally?

freud: A software suite for high throughput analysis of particle simulation data

Analyzing Particle Systems for Machine Learning and Data Visualization with freud

Contact Info

Product

Resources

About