Michael F. Herbst

Algorithmic differentiation eliminates the nightmare of deriving analytical second derivatives for meta-GGAs, unlocking fully differentiable perturbation theory for end-to-end training of neural-network exchange-correlation functionals.

Lower semicontinuity of the kinetic-energy functional unlocks a robust inversion scheme for recovering exchange-correlation potentials in periodic systems.