Towards self-driving laboratories in chemistry and materials sciences: The central role of DFT in the era of AI

Abstract: Density functional theory plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and low computational cost. We review recent progress in machine learning model developments, which has relied heavily on density functional theory for synthetic data generation and model architecture, and provide some broader context for its general relevance to the chemical sciences. Resulting in models with high efficiency, accuracy, scalability, and transferability (EAST), these developments will pave the way for the routine use of successful experimental planning software within self-driving laboratories.