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AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials

Soft Matter(2023)10.1039/d3sm00402cSource: DataRank Database

AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials is a research paper published in Soft Matter (2023). On theSindex it has a DataRank of 0. It has been cited 15 times.

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Open Access15 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (16 grants)Impact (FWCI 1.69)OA: hybridTopics (19)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (7)

Sotirios Kakaletsis ORCID,Xuesong Zhang ORCID,Johannes Weickenmeier ORCID,Edward Castillo ORCID,Berkin Dortdivanlioglu ORCID

Live enrichment

PubMed:37622379 PMC:PMC10499084 OpenAlex:https://openalex.org/W4386153284

National Science Foundation

Grant: CMMI 2046148

National Science Foundation

Grant: CMMI 1916663

National Science Foundation

Grant: CMMI 2127925

National Science Foundation

Grant: DMR 2105175

National Science Foundation

Grant: CMMI 2119716

National Science Foundation

Grant: CMMI 1953323

National Institutes of Health

Grant: R01HL165251

National Institutes of Health

Grant: R21HL161832

National Institutes of Health

Grant: 1R21HL161832-01

Human-Specific Prediction, Training, and Visualization Tools for the Tricuspid Valve from Existing Data

National Science Foundation

Grant: 1953323

LEAP-HI: Tackling Brain Diseases with Mechanics: A Data-Driven Approach to Merge Advanced Neuroimaging and Multi-Physics Modeling

National Science Foundation

Grant: 1916663

Collaborative Research: An in vivo/in silico Approach to Delineate the Effect of Age on Pressure Ulcer Susceptibility

National Science Foundation

Grant: 2127925

Collaborative Research: Inferring The In Situ Micro-Mechanics of Embedded Fiber Networks by Leveraging Limited Imaging Data

National Science Foundation

Grant: 2105175

Understanding Mechano-Fibrinolysis: Fiber-Scale Multiphysics Experiments and Models

National Science Foundation

Grant: 2119716

DMREF/Collaborative Research: Computationally Driven Design of Synthetic Tissue-Like Multifunctional Materials

National Institutes of Health

Grant: 3R01HL165251-03S2

Using an Electrospun Surrogate Model to Investigate Mechanisms of Tricuspid Valve Maladaptation

National Science Foundation

Grant: 2046148

CAREER: Toward a Fundamental Understanding of Why Thrombus Dissolves, Persists, or Breaks Off

FWCI

1.69

Citation Percentile

0.8%

Citation Trend

2024

2025

2026

hybridLicense: cc-by

journalhttps://pubs.rsc.org/en/content/articlepdf/2023/sm/d3sm00402c

publisherhttps://pubs.rsc.org/en/content/articlepdf/2023/sm/d3sm00402c

publisherhttp://pubs.rsc.org/en/content/articlepdf/2023/SM/D3SM00402C

journalhttps://doi.org/10.1039/d3sm00402c

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37622379

Fields of Study

Advanced machining processes and optimizationForce Microscopy Techniques and ApplicationsMetal and Thin Film Mechanics0202 electrical engineering, electronic engineering, information engineering02 engineering and technologyMachine LearningNanotechnologyNeural Networks, Computer

MeSH Terms

Machine LearningNeural Networks, ComputerNanotechnology

Keywords

Nano-IndentationMaterials scienceNanotechnologyComposite materialMachine LearningChemistryNeural Networks, Computer