High-entropy ceramics are an emerging class of materials in which configurational entropy competes directly with enthalpy to stabilize complex crystal structures. Beyond their promise for applications ranging from energy storage and catalysis to radiation tolerance and extreme-environment structural materials, their vast compositional space and diverse bonding environments present a rich landscape for simulation and modeling.

In this talk, I will show how we use density functional theory and machine-learned interatomic potentials to probe—and ultimately control—bonding, magnetism, microstructure, interfacial melting, and defect dynamics in these systems. Focusing on oxides and transition metal carbides, I will highlight how computational approaches can transform chemical disorder from a source of complexity into a platform for predictive materials design.