Materials Science
Supervised machine learning for corrosion assessment of multi-principal element alloys using experimental and generative datasets
This research pioneers an integrated GAN-ML approach to enhance the prediction of corrosion properties in multi-principal element alloys (MPEAs). By leveraging generative adversarial networks (GANs) to augment scarce experimental data, the study significantly improves the accuracy and generalizability of supervised machine learning (ML) models like Random Forest, Neural Networks, Kernel Ridge Regression, and LassoIC. This methodology addresses the complexity and vast compositional space of MPEAs, offering a computationally efficient data-driven framework to accelerate the design and discovery of corrosion-resistant alloys.
Leverage advanced AI to gain a competitive edge in materials innovation.
0.95 R²
Peak Predictive Accuracy (Epit)
+83%
Accuracy Improvement (RF, icorr)
200,000
Synthetic Data Points Generated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
MPEA Corrosion Prediction Workflow
Empirical Corrosion Testing
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Corrosion Properties Dataset
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Distribution of Existing Dataset
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Generative Design Model (NSGAN)
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Combined Experimental + GAN Dataset
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Machine Learning Models (RF, NN, KRR, LassoIC)
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GUI User Tool 'Corrosion Predictor'
Calculate Your Potential ROI
The innovative GAN-ML framework can significantly reduce the R&D cycle for new corrosion-resistant MPEAs. By accurately predicting material performance, it minimizes the need for extensive physical prototyping and experimental iterations, leading to faster market entry and substantial cost savings in materials development.
Annual Savings Potential
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A typical timeline for integrating GAN-ML for materials R&D, tailored for enterprise success.
Data Harmonization & Feature Engineering
Duration: 4-6 Weeks
Consolidate diverse MPEA corrosion datasets and engineer robust features incorporating compositional, microstructural, and environmental factors crucial for ML model training.
NSGAN Model Development & Data Augmentation
Duration: 8-12 Weeks
Develop and train the NSGAN framework to generate high-fidelity synthetic data, addressing scarcity and improving the comprehensiveness of the training dataset.
Supervised ML Model Training & Optimization
Duration: 6-10 Weeks
Train and fine-tune various supervised ML models (RF, NN, KRR, LassoIC) using the augmented dataset, focusing on hyperparameter optimization and cross-validation for maximum predictive accuracy.
GUI Development & Deployment
Duration: 4-8 Weeks
Build an intuitive graphical user interface (GUI) for the 'Corrosion Predictor' tool, enabling easy input of alloy parameters and real-time prediction of corrosion properties, followed by deployment for user access.
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Discover how our AI-driven solutions can revolutionize your R&D for advanced materials. Schedule a personalized consultation to explore tailored strategies for integrating GAN-ML into your enterprise, optimize alloy design, and gain a competitive edge in corrosion-resistant materials.
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