BetaCitation Atlas

RESEARCH ARTICLE · Coatings · 2025

Machine Learning-Based Prediction of Atmospheric Corrosion Rates Using Environmental and Material Parameters

Saurabh Tiwari, K. Dash, Nokeun Park, N.S. Reddy

Read original paper ↗

See where this research leads.

7 direct citing records in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · Saved source query complete
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
7 direct citing records saved

Machine Learning-Based Prediction of Atmospheric Corrosion Rates Using Environmental and Material ParametersOriginalSmart and green corrosion inhibitors: Mechanisms, computational tools, and sustainable protection strategies13The Performance of Accelerated Testing Methods for the Evaluation of Weathering Steels3Machine learning in corrosion electrochemistry: A critical review of prediction and corrosion-type identification8Machine learning applications in metallic materials: Recent advances and future perspectives8Machine Learning-Based Prediction of Stacking Fault Energy in High-Manganese Steels: A Comparative Study of Ensemble and Kernel Methods2Predictive Modelling of Corrosion Inhibition from Eggshell-Derived Solutions Using Robust Machine Learning Algorithms0Toward data-driven reliability in hydrogel semiconductor devices: forecasting approaches for degradation and signal stability0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close8 nodes / 7 citation links in this initial view
Browse Matching Papers

Papers that cite this work

7 direct citing records saved.

  1. Smart and green corrosion inhibitors: Mechanisms, computational tools, and sustainable protection strategies

    Materials Chemistry and Physics · 2025

    Ahmed A. Al‐Amiery, Lina M. Shaker

    Read original paper ↗ ·

  2. The Performance of Accelerated Testing Methods for the Evaluation of Weathering Steels

    Metals · 2026

    Shazia Muzaffer, Mohammad Al‐Emrani

    Read original paper ↗ ·

  3. Machine learning in corrosion electrochemistry: A critical review of prediction and corrosion-type identification

    International Journal of Electrochemical Science · 2026

    Ruinan Guo, Junxiang Rong

    Read original paper ↗ ·

  4. Machine learning applications in metallic materials: Recent advances and future perspectives

    Journal of Alloys and Compounds · 2026

    Bin Miao, Guan-Qi Lin, Yuan Zhang, Yu-Wei Cheng, Huan Yang

    Read original paper ↗ ·

  5. Machine Learning-Based Prediction of Stacking Fault Energy in High-Manganese Steels: A Comparative Study of Ensemble and Kernel Methods

    Materials · 2026

    Saurabh Tiwari, Seong Jun Heo, Nokeun Park

    Read original paper ↗ ·

  6. Predictive Modelling of Corrosion Inhibition from Eggshell-Derived Solutions Using Robust Machine Learning Algorithms

    Research Square · 2026

    Victor Damilare Ola, Mysara Eissa Mohyaldinn, Ibnelwaleed A. Hussein, Abubakar Aji, Azubuike Hope Amadi, Aremu Ibraheem Adebayo, Isaac Jiya Majinko

    Read original paper ↗ ·

  7. Toward data-driven reliability in hydrogel semiconductor devices: forecasting approaches for degradation and signal stability

    Frontiers in Materials · 2026

    Geetha Anbazhagan, PREETHI S, J. Santhakumar

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.