# BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit

arXiv: https://arxiv.org/abs/2002.10401 · HTML: https://mlsanity.com/paper/2002.10401/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Machine learning for classifying and interpreting coherent X-ray speckle patterns](https://mlsanity.com/paper/2211.08194/) (2022)

## Abstract (arXiv)

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.