  A myriad of phenomena in materials science and chemistry rely on
quantum-level simulations of the electronic structure in matter. While moving
to larger length and time scales has been a pressing issue for decades, such
large-scale electronic structure calculations are still challenging despite
modern software approaches and advances in high-performance computing. The
silver lining in this regard is the use of machine learning to accelerate
electronic structure calculations -- this line of research has recently gained
growing attention. The grand challenge therein is finding a suitable
machine-learning model during a process called hyperparameter optimization.
This, however, causes a massive computational overhead in addition to that of
data generation. We accelerate the construction of neural network models by
roughly two orders of magnitude by circumventing excessive training during the
hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham
density functional theory, the most popular computational method in materials
science and chemistry.

Materials Science