ENMA 312: Experimental Methods in Materials Science

Guest lecturer for the computational methods module of this undergraduate experimental methods course, introducing students to machine-learning interatomic potentials as a tool for materials calculations alongside the course’s experimental techniques.

In the accompanying computation lab, students use the CHGNet machine-learning interatomic potential to:

• Optimize the crystal structure of solid Al and compare the calculated equilibrium volume to experiment
• Compute an equation of state (energy vs. volume) for Al and extract its bulk modulus
• Calculate the (de)intercalation voltage of a LiFePO4 cathode for Li-ion batteries and the associated volume expansion on cycling
• (Optional) Estimate the vacancy formation energy in Al and explore vacancy-vacancy interactions