Learning grain boundary segregation energy spectra in polycrystals
Abstract
The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.
- Publication Date:
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1816794
- Grant/Contract Number:
- SC0020180
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; atomistic models; computational methods; metals and alloys; surfaces, interfaces and thin films
Citation Formats
Wagih, Malik, Larsen, Peter M., and Schuh, Christopher A. Learning grain boundary segregation energy spectra in polycrystals. United States: N. p., 2020.
Web. doi:10.1038/s41467-020-20083-6.
Wagih, Malik, Larsen, Peter M., & Schuh, Christopher A. Learning grain boundary segregation energy spectra in polycrystals. United States. https://doi.org/10.1038/s41467-020-20083-6
Wagih, Malik, Larsen, Peter M., and Schuh, Christopher A. Fri .
"Learning grain boundary segregation energy spectra in polycrystals". United States. https://doi.org/10.1038/s41467-020-20083-6. https://www.osti.gov/servlets/purl/1816794.
@article{osti_1816794,
title = {Learning grain boundary segregation energy spectra in polycrystals},
author = {Wagih, Malik and Larsen, Peter M. and Schuh, Christopher A.},
abstractNote = {The segregation of solute atoms at grain boundaries (GBs) can profoundly impact the structural properties of metallic alloys, and induce effects that range from strengthening to embrittlement. And, though known to be anisotropic, there is a limited understanding of the variation of solute segregation tendencies across the full, multidimensional GB space, which is critically important in polycrystals where much of that space is represented. Here we develop a machine learning framework that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum—of solute atoms at GB sites in polycrystals, based solely on the undecorated (pre-segregation) local atomic environment of such sites. We proceed to use the learning framework to scan across the alloy space, and build an extensive database of segregation energy spectra for more than 250 metal-based binary alloys. The resulting machine learning models and segregation database are key to unlocking the full potential of GB segregation as an alloy design tool, and enable the design of microstructures that maximize the useful impacts of segregation.},
doi = {10.1038/s41467-020-20083-6},
journal = {Nature Communications},
number = 1,
volume = 11,
place = {United States},
year = {Fri Dec 11 00:00:00 EST 2020},
month = {Fri Dec 11 00:00:00 EST 2020}
}
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