A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra
Stacking fault energy (SFE) is a critical property governing deformation mechanisms and influencing the mechanical behavior of materials. This work presents a unified framework for understanding and predicting SFE based solely on an electronic structure representation. By integrating density of stat...
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MDPI AG
2025-04-01
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| author | Md Tohidul Islam Scott R. Broderick |
| author_facet | Md Tohidul Islam Scott R. Broderick |
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| description | Stacking fault energy (SFE) is a critical property governing deformation mechanisms and influencing the mechanical behavior of materials. This work presents a unified framework for understanding and predicting SFE based solely on an electronic structure representation. By integrating density of states (DOS) spectral data, dimensionality reduction techniques, and machine learning models, it was found that the SFE behavior is indeed represented within the electronic structure and that this information can be used to accelerate the prediction of SFE. In the first part of this study, we established quantitative relationships between electronic structure and microstructural features, linking chemistry to mechanical properties. Using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), we identified key features from high-resolution vector representation of DOS data and explored their correlation with SFE. The second part of this work focuses on the predictive modeling of SFE, where a machine learning model trained on UMAP-reduced features achieved high accuracy (R<sup>2</sup> = 0.86, MAE = 15.46 mJ/m<sup>2</sup>). To bridge length scales, we extended this methodology to predict SFE in alloy systems, leveraging single-element data to inform multi-element alloy design. We illustrate this approach with Cu-Zn alloys, where the framework enabled rapid screening of compositional space while capturing complex electronic structure interactions. The proposed framework accelerates alloy design by reducing reliance on costly experiments and ab initio calculations. |
| format | Article |
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| institution | OA Journals |
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| publishDate | 2025-04-01 |
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| series | Crystals |
| spelling | doaj-art-189dfe56dc954c3a908a37c4202ff76a2025-08-20T01:56:19ZengMDPI AGCrystals2073-43522025-04-0115539010.3390/cryst15050390A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States SpectraMd Tohidul Islam0Scott R. Broderick1Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USADepartment of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USAStacking fault energy (SFE) is a critical property governing deformation mechanisms and influencing the mechanical behavior of materials. This work presents a unified framework for understanding and predicting SFE based solely on an electronic structure representation. By integrating density of states (DOS) spectral data, dimensionality reduction techniques, and machine learning models, it was found that the SFE behavior is indeed represented within the electronic structure and that this information can be used to accelerate the prediction of SFE. In the first part of this study, we established quantitative relationships between electronic structure and microstructural features, linking chemistry to mechanical properties. Using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), we identified key features from high-resolution vector representation of DOS data and explored their correlation with SFE. The second part of this work focuses on the predictive modeling of SFE, where a machine learning model trained on UMAP-reduced features achieved high accuracy (R<sup>2</sup> = 0.86, MAE = 15.46 mJ/m<sup>2</sup>). To bridge length scales, we extended this methodology to predict SFE in alloy systems, leveraging single-element data to inform multi-element alloy design. We illustrate this approach with Cu-Zn alloys, where the framework enabled rapid screening of compositional space while capturing complex electronic structure interactions. The proposed framework accelerates alloy design by reducing reliance on costly experiments and ab initio calculations.https://www.mdpi.com/2073-4352/15/5/390stacking fault energydensity of statesmachine learning (ML) in materials sciencealloy designdimensionality reduction |
| spellingShingle | Md Tohidul Islam Scott R. Broderick A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra Crystals stacking fault energy density of states machine learning (ML) in materials science alloy design dimensionality reduction |
| title | A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra |
| title_full | A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra |
| title_fullStr | A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra |
| title_full_unstemmed | A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra |
| title_short | A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra |
| title_sort | data driven framework for accelerated modeling of stacking fault energy from density of states spectra |
| topic | stacking fault energy density of states machine learning (ML) in materials science alloy design dimensionality reduction |
| url | https://www.mdpi.com/2073-4352/15/5/390 |
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