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A dropout uncertainty neural network (DUNN) model driver v000
Description A dropout uncertainty neural network (DUNN) potential model driver, which supports running in both fully-connected mode and dropout mode. The DUNN can be used easily to quantify the uncertainty in atomistic simulations and determine the transferability of potential.
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Contributor Mingjian Wen
Maintainer Mingjian Wen
Published on KIM 2019
How to Cite

This Model Driver originally published in [1] is archived in OpenKIM [2-4].

[1] Wen M, Tadmor EB. Uncertainty quantification in molecular simulations with dropout neural network potentials. npj Computational Materials. 2020;6(1). doi:10.1038/s41524-020-00390-8

[2] Wen M. A dropout uncertainty neural network (DUNN) model driver v000. OpenKIM; 2019. doi:10.25950/9573ca43

[3] Tadmor EB, Elliott RS, Sethna JP, Miller RE, Becker CA. The potential of atomistic simulations and the Knowledgebase of Interatomic Models. JOM. 2011;63(7):17. doi:10.1007/s11837-011-0102-6

[4] Elliott RS, Tadmor EB. Knowledgebase of Interatomic Models (KIM) Application Programming Interface (API). OpenKIM; 2011. doi:10.25950/ff8f563a

Click here to download the above citation in BibTeX format.
Funding Not available
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DOI 10.25950/9573ca43
KIM Item TypeModel Driver
KIM API Version2.0

DUNN__MD_292677547454_000.txz Tar+XZ Linux and OS X archive
DUNN__MD_292677547454_000.zip Zip Windows archive
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