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Learning grain boundary segregation energy spectra in polycrystals
Malik Wagih1, Peter M Larsen2, Christopher A Schuh3
1Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Nature Communications
|December 14, 2020
Summary
Understanding solute segregation at grain boundaries (GBs) is key for alloy design. This study uses machine learning to predict segregation tendencies, creating a database for advanced materials development.
Area of Science:
- Materials Science
- Computational Materials Science
- Alloy Design
Background:
- Solute atom segregation at grain boundaries (GBs) significantly influences metallic alloy properties, affecting strengthening and embrittlement.
- The anisotropic nature of solute segregation and its variation across multidimensional GB space in polycrystals remain poorly understood.
- This knowledge gap limits the effective use of GB segregation as a tool for designing advanced metallic alloys.
Purpose of the Study:
- To develop a machine learning (ML) framework for predicting solute atom segregation tendencies at GB sites.
- To quantify segregation tendency using the segregation enthalpy spectrum based on the local atomic environment.
- To build an extensive database of segregation energy spectra for numerous metal-based binary alloys.
Main Methods:
- Development of a novel machine learning framework.
- Prediction of segregation tendency solely from the pre-segregation local atomic environment of GB sites.
- Scanning the alloy space to generate a comprehensive database of segregation energy spectra for over 250 binary alloys.
Main Results:
- Accurate prediction of solute segregation tendencies at GBs using the developed ML framework.
- Creation of an extensive database detailing segregation energy spectra for a wide range of metal-based binary alloys.
- Demonstration that ML models can predict segregation based on local atomic structure.
Conclusions:
- The developed ML framework and segregation database are crucial for leveraging GB segregation in alloy design.
- This approach enables the rational design of microstructures to optimize the beneficial effects of solute segregation.
- Advances in computational materials science pave the way for designing alloys with tailored properties through precise control of GB segregation.
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