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1
Introduction
2
Agenda
3
About Julia
4
What makes Julia nice
5
Flexibility
6
Density Functional Toolkit
7
Algorithmic Differentiation
8
Ecosystem
9
Molly
10
Veneerization
11
Graphene
12
sesmix
13
Conclusion
14
Multidispatch
15
Constraints
16
Mojo
17
Importing Python Models
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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.

Julia for Materials Modelling

Materials Cloud
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