Interatomic Potentials for Materials

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Data Driven Science: MODNet Methodology

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

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Interatomic Potentials: LJ and EAM

Explores LJ and EAM interatomic potentials, fitting to material properties and challenges in empirical potential fitting.

Berni Alder Prize: Celebrating 50 Years of CECAM

Commemorates 50 years of CECAM and the Berni J. Alder CECAM Prize, covering milestones in computational methods, quantum mechanics, slip motion, and more.

Atomic-Scale Modeling: Introduction & Hands-On

Offers a hands-on introduction to atomic-scale modeling through Jupyter notebooks, focusing on fundamental materials science concepts.

Chemistry: Atomic Structure and Thermodynamics

Covers atomic structure, thermodynamics, material properties, and ideal gas law.

Data-driven Manufacturing: Additive Manufacturing Focus

Covers data-driven manufacturing with a focus on additive manufacturing, including manufacturability analysis and a machine learning approach.

Computational Methods: Paths and Strings

Covers computational methods focusing on paths and strings, including examples of concatenation, regex elements, and string operations.

BDEs as a Measure of Radical Stability

Explores bond dissociation energies as a measure of radical stability and the impact of substituents on BDE values.