## 3 Experimental Setup  
### Dataset  
The JARVIS-DFT Dataset [44] is a comprehensive resource for materials informatics, providing density functional theory (DFT) calculations for a variety of materials properties. It includes data on structural, electronic, and mechanical characteristics of materials, enabling advanced model training for predicting material behaviors. The dataset spans a wide range of material types and compositions, making it highly valuable for the development of machine learning models in computational materials science. The Zenodo Datasets [45] are a collection of open-access datasets across various scientific domains, including machine learning and materials science. They feature curated collections that emphasize reproducibility and data accessibility. For materials science, these datasets often include annotated material properties, facilitating data-driven discovery and model evaluation in diverse applications. The ICME Dataset [46] is curated to support Integrated Computational Materials Engineering (ICME), integrating experimental and simulation data for material property predictions. It covers various materials and microstructure-property relationships, making it a critical asset for interdisciplinary research. This dataset bridges the gap between theory and application by combining comprehensive experimental datasets with high-fidelity computational models. The Materials Project dataset [47] focuses on computationally derived material properties, leveraging high-throughput DFT calculations. It encompasses over 100,000 materials, with data on electronic structure, thermodynamics, and mechanical properties. This dataset is pivotal in accelerating materials discovery and design, as it provides an extensive repository for training machine learning algorithms in the field of computational materials science.  
In this study, we utilized four benchmark datasets: JARVIS-DFT, Zenodo, ICME, and the Materials Project. Each dataset contains structural, mechanical, and material property data relevant to stress-strain modeling. For JARVIS-DFT, we selected the subset containing density functional theory (DFT)-calculated mechanical properties, ensuring a diverse representation of material behaviors. From Zenodo, we focused on curated structural integrity datasets that provide experimental validation for predictive models. The ICME dataset was leveraged for its integrated computational-experimental framework, particularly in microstructure-property relationships. The Materials Project dataset was used to extract electronic structure and thermodynamic properties relevant to material deformation. To ensure consistency across datasets, we applied a standardized preprocessing pipeline. This included min-max normalization for numerical features to maintain uniform scaling, removal of redundant or missing data points, and data augmentation techniques such as random cropping and rotation for image-based inputs. We addressed potential class imbalances by employing stratified sampling and weighted loss functions where necessary. This preprocessing ensured that the datasets were well-aligned for training, validation, and testing of the proposed model, enhancing generalization and robustness across different material conditions.