Convolutional Kernel-Embedded <i>E</i> (3)-Equivariant Networks (KEN): Overcoming Architectural Rigidity in Machine Learning Interatomic Potentials

Jha Gautam

Open source

DOI: 10.1021/acs.jctc.6c01109
Published: 2026-10-02
Container: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
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. Source: Crossref; license: CC0 metadata
supportingDOI resolves: A matching record was returned by Crossref. Source: Crossref; license: CC0 metadata
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. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
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. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredOpen access status: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
supportingMetadata completeness: All 6 scored descriptive metadata groups are present. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c01109,
  title = {Convolutional Kernel-Embedded
                    <i>E</i>
                    (3)-Equivariant
Networks (KEN): Overcoming Architectural Rigidity in Machine Learning
Interatomic Potentials},
  author = {Jha Gautam},
  year = {2026},
  journal = {Journal of Chemical Theory
and Computation},
  doi = {10.1021/acs.jctc.6c01109},
  url = {https://doi.org/10.1021/acs.jctc.6c01109}
}

RIS

TY  - JOUR
TI  - Convolutional Kernel-Embedded
                    <i>E</i>
                    (3)-Equivariant
Networks (KEN): Overcoming Architectural Rigidity in Machine Learning
Interatomic Potentials
AU  - Jha Gautam
PY  - 2026
JO  - Journal of Chemical Theory
and Computation
DO  - 10.1021/acs.jctc.6c01109
UR  - https://doi.org/10.1021/acs.jctc.6c01109
ER  -

APA

Gautam, J. (2026). Convolutional Kernel-Embedded <i>E</i> (3)-Equivariant Networks (KEN): Overcoming Architectural Rigidity in Machine Learning Interatomic Potentials. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c01109

Source records

crossref · retrieved 2026-11-11T01:20:46.287Z