  Tensor cores, along with tensor processing units, represent a new form of
hardware acceleration specifically designed for deep neural network
calculations in artificial intelligence applications. Tensor cores provide
extraordinary computational speed and energy efficiency, but with the caveat
that they were designed for tensor contractions (matrix-matrix multiplications)
using only low-precision floating point operations. In spite of this, we
demonstrate how tensor cores can be applied with high efficiency to the
challenging and numerically sensitive problem of quantum-based Born-Oppenheimer
molecular dynamics, which requires highly accurate electronic structure
optimizations and conservative force evaluations. The interatomic forces are
calculated on-the-fly from an electronic structure that is obtained from a
generalized deep neural network, where the computational structure naturally
takes advantage of the exceptional processing power of the tensor cores and
allows for high performance in excess of 100 Tflops on the tensor cores of a
single Nvidia A100 GPU. Stable molecular dynamics trajectories are generated
using the framework of extended Lagrangian Born-Oppenheimer molecular dynamics,
which combines computational efficiency with long-term stability, even when
using approximate charge relaxations and force evaluations that are limited in
accuracy by the numerically noisy conditions caused by the low precision tensor
core floating-point operations. A canonical ensemble simulation scheme is also
presented, where the additional numerical noise in the calculated forces is
absorbed into a Langevin-like dynamics.
