Efficient calculations of electronic structures with machine-learning models

Quantum mechanical calculations of the electronic structure of matter enable accessing interesting thermodynamical properties without the need for prior experimental measurements.
Therefore, electronic structure calculations are of great interest in fields such as materials discovery or drug design. At the forefront of such simulations lies density functional theory (DFT), due to its excellent balance between computational accuracy and efficiency. Yet, as pressing environmental and social issues shift the research focus to increasingly complicated systems and conditions, even the most efficient of DFT implementations are approaching their limitations in terms of computational feasibility. A possible route to enable more complex calculations lies with machine learning (ML), i.e., algorithms that are capable of capturing complicated relationships based on large amounts of data.