Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores
Abstract
Structure-property relationships are a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore (A2B2O7) in a regime in which amorphization occurs as a consequence of defect accumulation. We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction, but when the machine learning model is restricted to only the B = Ti pyrochlores, the energetics of disordering and amorphization are critical factors. We discuss how these static quantities provide insight into an inherently kinetic property such as amorphization resistance at finite temperature. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight.
- Authors:
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC). Basic Energy Sciences (BES) (SC-22); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1348946
- Alternate Identifier(s):
- OSTI ID: 1361477; OSTI ID: 1508075
- Report Number(s):
- LA-UR-16-23806
Journal ID: ISSN 0897-4756
- Grant/Contract Number:
- AC52-06NA25396; EP/L005581/1; EP/L006170/1
- Resource Type:
- Published Article
- Journal Name:
- Chemistry of Materials
- Additional Journal Information:
- Journal Name: Chemistry of Materials Journal Volume: 29 Journal Issue: 6; Journal ID: ISSN 0897-4756
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY
Citation Formats
Pilania, Ghanshyam, Whittle, Karl R., Jiang, Chao, Grimes, Robin W., Stanek, Christopher R., Sickafus, Kurt E., and Uberuaga, Blas Pedro. Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores. United States: N. p., 2017.
Web. doi:10.1021/acs.chemmater.6b04666.
Pilania, Ghanshyam, Whittle, Karl R., Jiang, Chao, Grimes, Robin W., Stanek, Christopher R., Sickafus, Kurt E., & Uberuaga, Blas Pedro. Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores. United States. https://doi.org/10.1021/acs.chemmater.6b04666
Pilania, Ghanshyam, Whittle, Karl R., Jiang, Chao, Grimes, Robin W., Stanek, Christopher R., Sickafus, Kurt E., and Uberuaga, Blas Pedro. Fri .
"Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores". United States. https://doi.org/10.1021/acs.chemmater.6b04666.
@article{osti_1348946,
title = {Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores},
author = {Pilania, Ghanshyam and Whittle, Karl R. and Jiang, Chao and Grimes, Robin W. and Stanek, Christopher R. and Sickafus, Kurt E. and Uberuaga, Blas Pedro},
abstractNote = {Structure-property relationships are a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore (A2B2O7) in a regime in which amorphization occurs as a consequence of defect accumulation. We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction, but when the machine learning model is restricted to only the B = Ti pyrochlores, the energetics of disordering and amorphization are critical factors. We discuss how these static quantities provide insight into an inherently kinetic property such as amorphization resistance at finite temperature. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight.},
doi = {10.1021/acs.chemmater.6b04666},
journal = {Chemistry of Materials},
number = 6,
volume = 29,
place = {United States},
year = {Fri Mar 03 00:00:00 EST 2017},
month = {Fri Mar 03 00:00:00 EST 2017}
}
https://doi.org/10.1021/acs.chemmater.6b04666
Web of Science
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