Training-free hyperparameter optimization of neural networks for electronic structures in matter

Abstract

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.

Physics-enhanced neural networks for equation-of-state calculations

“…A second consequence of the existence of larger databases is that they offer greater possibilities for the application of machine learning techniques such as neural networks. The use of neural networks in scientific fields such as materials science [62,63], quantum physics and chemistry [64][65][66][67][68], high-energy-density physics [69], and even in WDM [70][71][72], is becoming increasingly popular. There are many reasons behind this, but relevant to this paper (besides the aforementioned growth in the size and number of databases) is the fact that they are excellent function approximators, with the ability to learn complex non-linear relationships between benchmark data and input features [73,74].…”