  Molecular dynamics (MD) is a powerful and popular tool for understanding the
dynamical evolution of materials at the nano and mesoscopic scales. There are
various flavors of MD ranging from the high fidelity albeit computationally
expensive ab-initio MD to relatively lower fidelity but much more efficient
classical MD such as atomistic and coarse-grained models. Each of these
different flavors of MD have been independently used by materials scientists to
bring about breakthroughs in materials discovery and design. A significant gulf
exists between the various MD flavors, each having varying levels of fidelity.
The accuracy of DFT or ab-initio MD is generally much higher than that of
classical atomistic simulations which is higher than that of coarse-grained
models. Multi-fidelity scale bridging to combine the accuracy and flexibility
of ab-initio MD with efficiency classical MD has been a longstanding goal. The
advent of big-data analytics has brought to the forefront powerful machine
learning methods that can be deployed to achieve this goal. Here, we provide
our perspective on the challenges in multi-fidelity scale bridging and trace
the developments leading up to the use of machine learning algorithms and
data-science towards addressing this grand challenge.

Computational Physics;Materials Science