Size transferability of machine-learning based density functional theory surrogates

Density Functional Theory (DFT) is the most common tool for investigating materials under extreme conditions, yet its scaling behavior with respect to both system size and temperature prohibits large scale simulations in such regimes. Progress in this regard would enable accurate modeling of planetary interiors or radiation damage in fusion reactor walls.
One possible route to alleviate these scaling problems is through the use of surrogate models, i.e., machine-learning models. These are trained on DFT data and are able to reproduce DFT predictions of energies and forces at comparable accuracy, but negligible computational cost.
Yet, in order to avoid repeated costly training data generation, models need to be able to transfer across length scales. Here, we present such transferability results. They show how learning local information can allow models to extrapolate to length scales that are not attainable with standard DFT methods. The models are based upon the Materials Learning Algorithms (MALA) package [1] and the therein implemented LDOS based machine learning workflow [2].

[1]: https://github.com/mala-project
[2]: J. A. Ellis et al., Phys. Rev. B 104, 035120, 2021