CHE-5424: Computational and Data Sciences for Materials Design

Description: Computational modeling and data science techniques for the design of new materials. Data curation from material databases and literature. Data generation by multiscale simulations of materials. Supervised and unsupervised machine learning models. Evolutionary optimization and reinforcement learning algorithms, materials property prediction, materials screening from existing databases, inverse design of materials with desired properties. Advantages and limitations of different approaches and assessment of different steps involved in materials discovery.

Pathways: N/A

Course Hours: 3 credits

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Crosslist: N/A

Repeatability: N/A