This work aimed to address the challenge of developing holistic theoretical framework that can predict the corrosion inhibition efficiency and evaluate the anti-corrosion potentials of novel materials by integrating machine learning-based Quantitative Structure–Property Relationship (QSPR) models with computational simulation techniques.
Accurately predicting corrosion rates in low-alloy steels is a significant challengein materials engineering due to the intricate and nonlinear interaction between environmental exposure conditions and alloying elements. Conventional statistical corrosion models are by and large based on linear assumptions, and thereby...
Mohanad S. Hasan, A. Bader, Saad Shauket Sammen· Applied Chemistry for Engine...· 0 citations
The present study aims to identify the best suitable Machine Learning (ML) model to predict the Electrical Conductivity (EC) and also to determine the most influencing feature parameter on the EC. In the view of Electromagnetic Interference (EMI) shielding materials, the EC property is an important parameter for elec...
Satish Geeri· Journal of composite materia...· 0 citations
Accurate prediction of shale methane adsorption capacity is crucial for reservoir evaluation. This study integrates 486 experimental datasets to develop a multivariate machine learning prediction model. Six key geological parameters, including depth, total organic carbon (TOC), moisture, porosity, vitrinite reflectan...
Hong-Jian Zhu, Ning Zhang, Zong-Quan Hu et al.· Frontiers in Earth Science· 0 citations
The efficiency of metal oxide photocatalysis is constrained by complex, nonlinear interactions among variables that are difficult to predict via traditional experimental methods. This study develops a robust Artificial Neural Network (ANN) model to identify the optimal training algorithm and architecture for predicting...
Muhammad Akmal Faiz Bin Rizalmy, Auni Kiasatina Azman, Mohd Fadhil Majnis et al.· 2026 7th International Confe...· 0 citations
Optimizing layered double hydroxides (LDHs) for phosphate removal is challenged by complex, multivariable interactions. To bridge this knowledge gap, this study introduces a novel data‐driven framework leveraging a comprehensive 2251‐record dataset and advanced machine learning to predict and optimize LDHs adsorption c...
Jian-Peng Jiang, Ying-Jun Xiong· Water environment research· 0 citations
To address the complexity and high cost of traditional approaches for metal-modified LiBH4 systems, this study proposes a small-sample ensemble learning-density functional theory (EL-DFT) framework for the efficient prediction and mechanistic analysis of hydrogen dissociation energies (
E
d
) in bimetal-doped stru...
Zi-Shan Luo, Jia-Wei Li, Wen-Hao Yan et al.· E3S Web of Conferences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
