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BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit
[article]
2020
arXiv
pre-print
Preserved Fulltext
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 ...
Here, we present our automated framework (BLAST -Bridging Length/Time scales via Atomistic Simulation Toolkit) that allows users to create their own models by generating training data sets, optimizing ...
arXiv:2002.10401v1
fatcat:fmskugm2erhnlctf3vtukmef4a
Machine Learning for Multi-fidelity Scale Bridging and Dynamical Simulations of Materials
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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. ...
In this regards, Chan and coworkers developed an autonomous framework termed BLAST-Bridging Length/Time scales via Atomistic Simulation Toolkit-that allows users to create their own potentials/FFs by following ...
arXiv:2004.00232v1
fatcat:bwlk65kbwzfm5hmm5fqie65pxe