  A direct and local deep learning (DL) model for atomic forces is presented.
We demonstrate the model performance in bulk aluminum, sodium, and silicon; and
show that its errors are comparable to those found in state-of-the-art machine
learning and DL models. We then analyze the model's performance as a function
of the number of neighbors included and show that one can ascertain physical
attributes of the system from the analysis of the deep learning model's
behavior. Finally, we test the size scaling performance of the model, and the
transferability between different temperatures, and show that our model
performs well in both scaling to larger systems and high-to-low temperature
predictability.
