Data Science for Assembly Engineering - Sharon C Glotzer

Explore data science applications in assembly engineering through this 56-minute conference talk by Sharon C Glotzer at KDD. Delve into key concepts such as self-assembly, crystallization, and complex crystal structures in soft matter. Discover the role of computer simulations, molecular dynamics, and open-source practices in studying nanoparticle shapes and clathrate colloidal crystals. Learn about the Signiac Framework for research, best practices in file naming, and data visualization techniques. Gain insights into machine learning applications for structural description, continuous topology, and self-assembly pathways. Enhance your understanding of how data science tools can be leveraged to advance assembly engineering and crystal structure analysis.

Syllabus

Introduction
What is assembly
Selfassembly
Crystallization
Examples of crystals
Complex crystal structures
Soft matter
Example
Title
Clathrate colloidal crystals
Nanoparticle shapes
Computer simulations
Molecular dynamics
Open Source
Best Practices
Signiac Framework
Signiac Research
File Naming
Sadiak
Data Visualization
Machine Learning on Data
Machine Learning Website
Machine Learning Example
Structural Describing
Continuous Topology
MNIST
Selfassembly pathway
Crystal structures
Summary
Conclusion
Questions