• Explain the fundamentals of ML and its relevance to materials science.
• Analyze and manipulate materials datasets using Python tools such as Numpy, Pandas, and Matplotlib.
• Use Pymatgen to analyze and manipulate crystal structures.
• Access and use APIs like the Materials Project for data retrieval.
• Identify and categorize common data types in materials science from both experimental and computational sources.
• Perform featurization of materials data for use in ML models.
• Interpret the output of ML models in the context of materials discovery and design.
• Train ML models with packages like scikit-learn and PyTorch.
• Design and implement research projects applying ML to real-world materials problems.
• Explore the application of deep learning, machine learning interatomic potentials, and generative AI in materials science.
• Build foundational Python programming skills, including object-oriented programming.
• Use GitHub and VSCode for version control, collaboration, and project development.