  In the present paper, we introduce a new neural network-based tool for the
prediction of formation energies of atomic structures based on elemental and
structural features of Voronoi-tessellated materials. We provide a concise
overview of the connection between the machine learning and the true
material-property relationship, how to improve the generalization accuracy by
reducing overfitting, how new data can be incorporated into the model to tune
it to a specific material system, and preliminary results on using models to
preform local structure relaxations.
  The present work resulted in three final models optimized for (1) highest
test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in
the discovery of new materials, and (3) performance at a low computational
cost. On a test set of 21,800 compounds randomly selected from OQMD, they
achieve a mean absolute error (MAE) of 28, 40, and 42 meV/atom, respectively.
The second model provides better predictions in a test case of interest not
present in the OQMD, while the third reduces the computational cost by a factor
of 8.
  We collect our results in a new open-source tool called SIPFENN
(Structure-Informed Prediction of Formation Energy using Neural Networks).
SIPFENN not only improves the accuracy beyond existing models but also ships in
a ready-to-use form with pre-trained neural networks and a GUI interface. By
virtue of this, it can be included in DFT calculations routines at nearly no
cost.
