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Machine learning predicts molten chloride corrosion with 85% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine-learning framework to predict molten chloride corrosion rates using historical experimental data. The model, trained on 291 literature-reported experiments, achieved a cross-validated R^2 of 0.85 and an MAE of 0.41 mg cm^-2 day^-1. A novel Chloride Salt Preparation Index (CSPI) was introduced to quantify salt conditions, proving to be a dominant descriptor in the model. The framework also provides uncertainty estimates and highlights areas for improved data reporting and future research. AI

IMPACT Provides a new method for predicting material degradation in harsh environments, potentially aiding in the design of more resilient materials for industrial applications.

RANK_REASON Academic paper published on arXiv detailing a new machine learning model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning predicts molten chloride corrosion with 85% accuracy

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Academic paper published on arXiv detailing a new machine learning model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Md Mahmudul Hasan Pathik ·

    Data-Driven Modeling of Molten Chloride Corrosion Using Static Test Data

    arXiv:2611.07125v1 Announce Type: new Abstract: Molten chloride corrosion has been studied for decades, yet reliable prediction across different alloys, salts, and test conditions remains difficult. In this work, 291 static molten chloride corrosion experiments reported in the li…

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