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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.
A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.
Contributor Mwen
Maintainer Mwen
Creator Mingjian Wen
Publication Year 2019
Item Citation

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. 2019;

[2] 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
Short KIM ID
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Extended KIM ID
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DOI 10.25950/9573ca43
KIM Item TypeModel Driver
KIM API Version2.0
Programming Language(s)
The programming languages used in the code and the percentage of the code written in each one.
97.47% C++
2.53% Shell


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


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