  Constructing an accurate atomistic model for the high-pressure phases of tin
(Sn) is challenging because properties of Sn are sensitive to pressures. We
develop machine-learning-based deep potentials for Sn with pressures ranging
from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we
find the deep potential, which is obtained by training the ab initio data from
density functional theory calculations with the state-of-the-art SCAN
exchange-correlation functional, is suitable to characterize high-pressure
phases of Sn. We systematically validate several structural and elastic
properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc
structures of Sn, as well as the structural and dynamic properties of liquid
Sn. The thermodynamics integration method is further utilized to compute the
free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the
deep potential successfully predicts the phase diagram of Sn including the
existence of the triple-point that qualitatively agrees with the experiment.
