Abstract
The ever-increasing power of supercomputers coupled with highly scalable simulation codes has 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/Timescales via Atomistic Simulation Toolkit) that leverages machine learning principles to address this challenge. BLAST is a multi-fidelity scale bridging framework that provides 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 simulation 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.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
Use of the Center for Nanoscale Materials was supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. This work utilized the Carbon cluster at the facility for the framework development.
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Chan, H., Narayanan, B., Cherukara, M. et al. BLAST: bridging length/timescales via atomistic simulation toolkit. MRS Advances 6, 21–31 (2021). https://doi.org/10.1557/s43580-020-00002-z
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DOI: https://doi.org/10.1557/s43580-020-00002-z