MACHINE LEARNING IN MATERIALS

MS&E 760

Hands-on experience in modern tools of atomic and molecular modeling, including density functional theory, interatomic potentials, and molecular dynamics. Select additional/advanced techniques like high-throughput calculations, (Kinetic) Monte Carlo, accelerated molecular dynamics, and machine learning.

MS&E 401

Special topics of interest to students in materials science and engineering.

MS&E 551

Atomic, nanoscale and microscale structure of materials. Course is designed for first year graduate students with interests in materials research.

MS&E 260

Provides overview of the field of Materials Science and Engineering, with significant design and hands-on components. Highlights different types of materials, with a focus on describing the extensive impact that Materials Science and Engineering has had on society. Small teams provide hands-on experience in materials design, synthesis, and processing and the fabrication of materials with desired properties and function.

MS&E 460

An introduction to the theoretical and computational tools for computational materials, with hands on homework and laboratories. Topics may include atomistic simulation (e.g., molecular dynamics), mesoscale simulation (e.g,. Phase field method), macroscale simulation (e.g., finite element method), thermodynamic and kinetic modeling (CALPHAD method), informatics (e.g., machine learning), and special topics (e.g., solar cell design, electronic device simulation, etc.)