Microstructure-based variational deep material networks for robust uncertainty quantification in materials digital twins
- DOI
- 10.1038/s41524-026-02054-5
- Published
- 2026-05-09
- Container
- npj Computational Materials
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1038/s41524-026-02054-5,
title = {Microstructure-based variational deep material networks for robust uncertainty quantification in materials digital twins},
author = {Andreas E. Robertson and Samuel B. Inman and Ashley T. Lenau and Ricardo A. Lebensohn and Dongil Shin and Brad L. Boyce and Rémi Dingreville},
year = {2026},
journal = {npj Computational Materials},
doi = {10.1038/s41524-026-02054-5},
url = {https://doi.org/10.1038/s41524-026-02054-5}
}RIS
TY - JOUR TI - Microstructure-based variational deep material networks for robust uncertainty quantification in materials digital twins AU - Andreas E. Robertson AU - Samuel B. Inman AU - Ashley T. Lenau AU - Ricardo A. Lebensohn AU - Dongil Shin AU - Brad L. Boyce AU - Rémi Dingreville PY - 2026 JO - npj Computational Materials DO - 10.1038/s41524-026-02054-5 UR - https://doi.org/10.1038/s41524-026-02054-5 ER -
APA
Robertson, A. E., Inman, S. B., Lenau, A. T., Lebensohn, R. A., Shin, D., Boyce, B. L., & Dingreville, R. (2026). Microstructure-based variational deep material networks for robust uncertainty quantification in materials digital twins. npj Computational Materials. https://doi.org/10.1038/s41524-026-02054-5
Source records
- crossref · retrieved 2026-11-07T23:52:08.572Z