  Density functional theory (DFT) plays a pivotal role for the chemical and
materials science due to its relatively high predictive power, applicability,
versatility and computational efficiency. We review recent progress in machine
learning model developments which has relied heavily on density functional
theory for synthetic data generation and for the design of model architectures.
The general relevance of these developments is placed in some broader context
for the chemical and materials sciences. Resulting in DFT based machine
learning models with high efficiency, accuracy, scalability, and
transferability (EAST), recent progress indicates probable ways for the routine
use of successful experimental planning software within self-driving
laboratories.
