  The ever-increasing power of supercomputers coupled with highly scalable
simulation codes have made molecular dynamics an indispensable tool in
applications ranging from predictive modeling of materials to computational
design and discovery of new materials for a broad range of applications.
Multi-fidelity scale bridging between the various flavors of molecular dynamics
i.e. ab-initio, classical and coarse-grained models has remained a
long-standing challenge. Here, we introduce our framework BLAST (Bridging
Length/time scales via Atomistic Simulation Toolkit) that leverages machine
learning principles to address this challenge. BLAST is a multi-fidelity scale
bridging framework that provide users with the capabilities to train and
develop their own classical atomistic and coarse-grained interatomic potentials
(force fields) for molecular simulations. BLAST is designed to address several
long-standing problems in the molecular simulations community, such as
unintended misuse of existing force fields due to knowledge gap between
developers and users, bottlenecks in traditional force field development
approaches, and other issues relating to the accuracy, efficiency, and
transferability of force fields. Here, we discuss several important aspects in
force field development and highlight features in BLAST that enable its
functionalities and ease of use.
