AI Uncovers Hidden Alloy Power #sciencefather #researcher #materialscience

 

🌟 Predicting Grain-Boundary Segregation with Machine Learning

Grain-boundary (GB) segregation of solute elements plays a crucial role in determining the properties of alloys ⚙️. From phase transformations 🔄 to mechanical strength 💪 and even the stability of nanocrystalline structures 🔬—this process lies at the heart of materials design.

Traditionally, density functional theory (DFT) 🧮 has been the go-to tool for calculating the site-specific segregation energies. While highly accurate, DFT is also computationally expensive ⏳, limiting its use for exploring a wide range of solute elements and alloy systems.

👉 Enter machine learning (ML) 🤖. By training ML models on DFT-generated data, researchers can now predict segregation energies faster and more efficiently.

🔑 Our Approach

• We combined structural descriptors of segregation sites 🏗️ with element-specific parameters of solutes 🧪.

• We applied cross-validation ✅ and extrapolation scores 📊 to identify the best-performing descriptor sets.

• The optimized ML model can then predict segregation energies for solutes not included in the original dataset 🚀.

🧩 Application to Tungsten (W)

We tested this approach on the segregation of transition metals in tungsten (W). Results showed:

• Excellent accuracy 🎯 compared to DFT and literature values.

• Robust predictions for elements outside the training set.

📂 Open Science Contribution

To support further research, we’ve made our codes and datasets publicly available 💻📦, enabling others to apply and extend our model.

✨ Why This Matters?

This ML-driven approach accelerates the design of advanced alloys 🔧, supporting innovations in energy, aerospace, and nanotechnology 🌍. By lowering computational barriers, we open the door to faster discoveries and smarter materials engineering.

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