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Abstract Understanding the large deformation behavior of materials under external forces is crucial for reliable engineering applications. The mechanical properties of materials depend on their underlying microstructures, which change over time during deformation. Experimental observation of these processes is time-consuming and influenced by various conditions. Therefore, we developed , a physics-based simulation tool to replicate the deformation process of cubic microstructures. can predict the evolution of texture, represented by the orientation distribution function (ODF), over time under various loads and strain rates. This software package can be run on both Windows and Linux operating systems. Unlike conventional crystal plasticity finite element software, offers a distinct advantage by rapidly generating deformed textures, as it bypasses incorporating grain morphology. Furthermore, comparisons with existing experimental and computational studies on texture evolution have demonstrated that this software seamlessly replicates real-world material processing conditions through a simple modification of a single input matrix. 

Abstract Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques in this field. However, the “black-box” nature of deep learning models often raises concerns about their interpretability and reliability. In this study, we propose XElemNet to explore the interpretability of ElemNet by applying a series of explainable artificial intelligence (XAI) techniques, focusing on post-hoc analysis and model transparency. The experiments with artificial binary datasets reveal ElemNet’s effectiveness in predicting convex hulls of element-pair systems across periodic table groups, indicating its capability to effectively discern elemental interactions in most cases. Additionally, feature importance analysis within ElemNet highlights alignment with chemical properties of elements such as reactivity and electronegativity. XElemNet provides insights into the strengths and limitations of ElemNet and offers a potential pathway for explaining other deep learning models in materials science. 

Abstract Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.