  We study a generalization performance of the machine learning (ML) model to
predict the atomic forces within the density functional theory (DFT). The
targets are the Si and Ge single component systems in the liquid state. To
train the machine learning model, Gaussian process regression is performed with
the atomic fingerprints which express the local structure around the target
atom. The training and test data are generated by the molecular dynamics (MD)
based on DFT. We first report the accuracy of ML forces when both test and
training data are generated from the DFT-MD simulations at a same temperature.
By comparing the accuracy of ML forces at various temperatures, it is found
that the accuracy becomes the lowest around the phase boundary between the
solid and the liquid states. Furthermore, we investigate the transferability of
ML models trained in the liquid state to temperature and pressure. We
demonstrate that, if the training is performed at a high temperature and if the
volume change is not so large, the transferability of ML forces in the liquid
state is high enough, while its transferability to the solid state is very low.
