A direct and local deep learning model for atomic forces in solids

We demonstrate a direct and local Deep Learning (DL) model for atomic forces. We apply this model for bulk aluminum, silicon and sodium and show that the model errors are comparable to other state of the art algorithms. Our model allows the calculation of forces in large cells using a training data that we built from smaller cells that were calculated with Density Functional Theory (DFT). In addition, we examine the question of temperature transferability of the model and show that we can train the model with data that was produced at a high temperature and then test it on data that was produced at lower temperatures. We also explore the physical properties of the system (e.g. number of nearest neighbors) effect on the model convergence with respect to some of its parameters. Finally, we discuss why the performance of such local models is better in some materials in comparison to others.