Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Adversarial training for dynamics matching in coarse-grained models

The Journal of Chemical Physics(2025)10.1063/5.0274785Source: DataRank Database

Adversarial training for dynamics matching in coarse-grained models is a research paper published in The Journal of Chemical Physics (2025). On theSindex it has a DataRank of 0. It has been cited 2 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

2 citations

Download PDF

Cite:

Linked data & code

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 0.64)OA: closedTopics (15)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (0/2)

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (2)

Gregory A. Voth ORCID,Yihang Wang ORCID

Live enrichment

PubMed:40919962 PMC:PMC12587174 OpenAlex:https://openalex.org/W4414064764

National Science Foundation

Grant: CHE-2102677

National Institutes of Health

Grant: 1S10OD028655-01

Beagle-3: A Shared GPU Cluster for Biomolecular Sciences

NIH HHS

Grant: S10 OD028655

FWCI

0.64

Citation Percentile

0.7%

Citation Trend

2026

closedLicense: CC BY NC ND

publisherhttps://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0274785/20685049/104105_1_5.0274785.pdf

journalhttps://doi.org/10.1063/5.0274785

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40919962

https://www.ncbi.nlm.nih.gov/pmc/articles/12587174

https://dx.doi.org/10.48550/arxiv.2504.06505

Fields of Study

Machine Learning in Materials ScienceProtein Structure and DynamicsBlock Copolymer Self-Assembly

Keywords

Adversarial systemDiscriminatorGenerator (circuit theory)Consistency (knowledge bases)Matching (statistics)TrajectoryReliability (semiconductor)Artificial neural networkChemical Physics (physics.chem-ph)Physics - Chemical PhysicsFOS: Physical sciencesArticle

Supplemental Materials

10.60893/figshare.jcp.29941118.v1AIP Publishing

Supplemental Materials

10.60893/figshare.jcp.29941118AIP Publishing