Explore the Julia programming language as an alternative to traditional scientific computing languages in this 59-minute conference talk. Gain insights into Julia's emergence, its broad user base, and its application in atomistic materials modelling. Learn about Julia's key features, including its compiled and high-level nature, flexibility, and ecosystem. Discover specific tools like the Density Functional Toolkit and Algorithmic Differentiation. Examine case studies in materials science, such as Molly and Veneerization. Understand Julia's multidispatch capabilities, constraints, and its relationship with Python models. Conclude with perspectives on Julia's potential in the multidisciplinary field of materials modelling.
Overview
Syllabus
Introduction
Agenda
About Julia
What makes Julia nice
Flexibility
Density Functional Toolkit
Algorithmic Differentiation
Ecosystem
Molly
Veneerization
Graphene
sesmix
Conclusion
Multidispatch
Constraints
Mojo
Importing Python Models
Taught by
Materials Cloud