Edge-Aware Graph Attention Model for Structural Optimization of High Entropy Carbides
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908698052395008 |
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| author | Mangalassery, Neethu Mohan Singh, Abhishek Kumar |
| author_facet | Mangalassery, Neethu Mohan Singh, Abhishek Kumar |
| contents | Predicting relaxed atomic structures of chemically complex materials remains a major computational challenge, particularly for high-entropy systems where traditional first-principles methods become prohibitively expensive. We introduce the edge-aware graph attention model, a physics-informed graph neural network tailored for predicting relaxed atomic structures of high-entropy systems. the edge-aware graph attention model employs chemically and geometrically informed descriptors that capture both atomic properties and local structural environments. To effectively capture atomic interactions, our model integrates a multi-head self-attention mechanism that adaptively weighs neighbouring atoms using both node and edge features. This edge-aware attention framework learn complex chemical and structural relationships independent of global orientation or position. We trained and evaluated the edge-aware GAT model on a dataset of carbide systems, spanning binary to high-entropy carbide compositions, and demonstrated its accuracy, convergence efficiency, and transferability. The architecture is lightweight, with a very low computational footprint, making it highly suitable for large-scale materials screening. By providing invariance to rigid-body transformations and leveraging domain-informed attention mechanisms, our model delivers a fast, scalable, and cost-effective alternative to DFT, enabling accelerated discovery and screening of entropy-stabilised materials. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_07358 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Edge-Aware Graph Attention Model for Structural Optimization of High Entropy Carbides Mangalassery, Neethu Mohan Singh, Abhishek Kumar Disordered Systems and Neural Networks Materials Science Predicting relaxed atomic structures of chemically complex materials remains a major computational challenge, particularly for high-entropy systems where traditional first-principles methods become prohibitively expensive. We introduce the edge-aware graph attention model, a physics-informed graph neural network tailored for predicting relaxed atomic structures of high-entropy systems. the edge-aware graph attention model employs chemically and geometrically informed descriptors that capture both atomic properties and local structural environments. To effectively capture atomic interactions, our model integrates a multi-head self-attention mechanism that adaptively weighs neighbouring atoms using both node and edge features. This edge-aware attention framework learn complex chemical and structural relationships independent of global orientation or position. We trained and evaluated the edge-aware GAT model on a dataset of carbide systems, spanning binary to high-entropy carbide compositions, and demonstrated its accuracy, convergence efficiency, and transferability. The architecture is lightweight, with a very low computational footprint, making it highly suitable for large-scale materials screening. By providing invariance to rigid-body transformations and leveraging domain-informed attention mechanisms, our model delivers a fast, scalable, and cost-effective alternative to DFT, enabling accelerated discovery and screening of entropy-stabilised materials. |
| title | Edge-Aware Graph Attention Model for Structural Optimization of High Entropy Carbides |
| topic | Disordered Systems and Neural Networks Materials Science |
| url | https://arxiv.org/abs/2512.07358 |