Publications / Other Report

Finding Electronic Structure Machine Learning Surrogates without Training

Fiedler, Lenz F.; Hoffmann, Nils H.; Mohammed, Parvez M.; Popoola, Gabriel A.; Yovell, Tamar Y.; Oles, Vladyslav O.; Ellis, Austin E.; Rajamanickam, Sivasankaran R.; Cangi, Attila -.

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 machine-learning surrogate 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.