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BLAST: Bridging Length/time scales via Atomistic Simulation Toolkit [article]

Henry Chan, Badri Narayanan, Mathew Cherukara, Troy D. Loeffler, Michael G. Sternberg, Anthony Avarca, Subramanian K. R. S. Sankaranarayanan
2020 arXiv   pre-print
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]

Rohit Batra, Subramanian Sankaranarayanan
2020 arXiv   pre-print
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