Multi-fidelity scale bridging between various flavors of molecular dynamics (i.e. ab-initio, classical and coarse-grained models) has remained a long-standing challenge. BLAST (Bridging Length/time scales via Atomistic Simulation Toolkit) is a framework that leverages machine learning principles to address this challenge.
Opportunity and Solution
BLAST provides users with the capabilities to train and develop their own classical atomistic and coarse-grained interatomic potentials (i.e., force fields) for molecular simulations. BLAST is designed to address several long-standing problems in the molecular simulation community, such as unintended misuse of existing force fields due to a 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. The BLAST architecture consists of a web user-friendly interface, front-end and back-end web services, and machine learning algorithms that run on high-performance computing (HPC) clusters.