Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation
The growth velocity of the crystal–melt interface during solidification is one of the important parameters that determine the crystal growth morphology. However, both experimental investigations and theoretical calculations are time-consuming and labor-intensive. Moreover, machine learning (ML)-base...
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MDPI AG
2025-05-01
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| Series: | Crystals |
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| author | Jia Wei Mingyu Zhang Shuai Li Shu Li |
| author_facet | Jia Wei Mingyu Zhang Shuai Li Shu Li |
| author_sort | Jia Wei |
| collection | DOAJ |
| description | The growth velocity of the crystal–melt interface during solidification is one of the important parameters that determine the crystal growth morphology. However, both experimental investigations and theoretical calculations are time-consuming and labor-intensive. Moreover, machine learning (ML)-based methods are severely limited by the limited amount of available experimental data. In this work, the crystal–melt interface velocity of four alloy systems under different values of undercooling was calculated by molecular dynamics simulation. The results showed a similar trend to the experimental data. A framework including molecular dynamics (MD) calculation and a transfer learning (TL) model was proposed to predict the interface velocity of binary alloys during free solidification. In order to verify the effectiveness of the model, eight ML models were constructed based on pure experimental data for model comparison. The prediction ability of the different models was assessed from two perspectives: interpolation and extrapolation. The results show that, regardless of whether it is interpolation or extrapolation, the TL model driven by both physical information and experimental data is superior to ML models driven solely by experimental data. The interpretability analysis method reveals the specific role of feature values in the interface velocity prediction of binary alloys. |
| format | Article |
| id | doaj-art-a1db124dcf9541b1a2c9d1ea4425acc0 |
| institution | DOAJ |
| issn | 2073-4352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Crystals |
| spelling | doaj-art-a1db124dcf9541b1a2c9d1ea4425acc02025-08-20T03:14:39ZengMDPI AGCrystals2073-43522025-05-0115548410.3390/cryst15050484Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics SimulationJia Wei0Mingyu Zhang1Shuai Li2Shu Li3School of Science, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Engineering Dielectric and Applications (Ministry of Education), School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Engineering Dielectric and Applications (Ministry of Education), School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaThe growth velocity of the crystal–melt interface during solidification is one of the important parameters that determine the crystal growth morphology. However, both experimental investigations and theoretical calculations are time-consuming and labor-intensive. Moreover, machine learning (ML)-based methods are severely limited by the limited amount of available experimental data. In this work, the crystal–melt interface velocity of four alloy systems under different values of undercooling was calculated by molecular dynamics simulation. The results showed a similar trend to the experimental data. A framework including molecular dynamics (MD) calculation and a transfer learning (TL) model was proposed to predict the interface velocity of binary alloys during free solidification. In order to verify the effectiveness of the model, eight ML models were constructed based on pure experimental data for model comparison. The prediction ability of the different models was assessed from two perspectives: interpolation and extrapolation. The results show that, regardless of whether it is interpolation or extrapolation, the TL model driven by both physical information and experimental data is superior to ML models driven solely by experimental data. The interpretability analysis method reveals the specific role of feature values in the interface velocity prediction of binary alloys.https://www.mdpi.com/2073-4352/15/5/484free dendrite growthinterface velocityundercoolingtransfer learningmolecular dynamics simulation |
| spellingShingle | Jia Wei Mingyu Zhang Shuai Li Shu Li Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation Crystals free dendrite growth interface velocity undercooling transfer learning molecular dynamics simulation |
| title | Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation |
| title_full | Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation |
| title_fullStr | Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation |
| title_full_unstemmed | Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation |
| title_short | Prediction of Dendrite Growth Velocity in Undercooled Binary Alloys Based on Transfer Learning and Molecular Dynamics Simulation |
| title_sort | prediction of dendrite growth velocity in undercooled binary alloys based on transfer learning and molecular dynamics simulation |
| topic | free dendrite growth interface velocity undercooling transfer learning molecular dynamics simulation |
| url | https://www.mdpi.com/2073-4352/15/5/484 |
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