Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction
Abstract Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adeq...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01599-1 |
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| author | Fei Shuang Kai Liu Yucheng Ji Wei Gao Luca Laurenti Poulumi Dey |
| author_facet | Fei Shuang Kai Liu Yucheng Ji Wei Gao Luca Laurenti Poulumi Dey |
| author_sort | Fei Shuang |
| collection | DOAJ |
| description | Abstract Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically. |
| format | Article |
| id | doaj-art-db72afefa45c44a58b16f85070d4226a |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-db72afefa45c44a58b16f85070d4226a2025-08-20T03:52:19ZengNature Portfolionpj Computational Materials2057-39602025-05-0111111210.1038/s41524-025-01599-1Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstructionFei Shuang0Kai Liu1Yucheng Ji2Wei Gao3Luca Laurenti4Poulumi Dey5Department of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2Department of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2Department of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2J. Mike Walker’66 Department of Mechanical Engineering, Texas A&M University, College StationDelft Centre of System and Control (DCSC), Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2Department of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2Abstract Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms. However, existing machine learning interatomic potentials (MLIPs) often fall short in adequately describing these defects, as their large characteristic scales exceed the computational limits of first-principles calculations. To address this challenge, we present a computational framework combining a defect genome constructed via empirical interatomic potential-guided sampling, with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations. The effectiveness of this approach was validated through simulations of nanoindentation, tensile deformation, and fracture in BCC tungsten. This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.https://doi.org/10.1038/s41524-025-01599-1 |
| spellingShingle | Fei Shuang Kai Liu Yucheng Ji Wei Gao Luca Laurenti Poulumi Dey Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction npj Computational Materials |
| title | Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction |
| title_full | Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction |
| title_fullStr | Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction |
| title_full_unstemmed | Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction |
| title_short | Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction |
| title_sort | modeling extensive defects in metals through classical potential guided sampling and automated configuration reconstruction |
| url | https://doi.org/10.1038/s41524-025-01599-1 |
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