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Title: Using machine learning to identify factors that govern amorphization of irradiated pyrochlores

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 (A 2B 2O 7) 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. Lastly, 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:
ORCiD logo [1] ;  [2] ;  [3] ;  [4] ; ORCiD logo [1] ;  [5] ; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of Liverpool (United Kingdom). School of Engineering
  3. Idaho National Lab. (INL), Idaho Falls, ID (United States). Fuels Modeling and Simulation Dept.
  4. Imperial College, London (United Kingdom). Dept. of Materials
  5. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Materials Science and Engineering
Publication Date:
Report Number(s):
LA-UR-16-23806
Journal ID: ISSN 0897-4756
Grant/Contract Number:
AC52-06NA25396; EP/L005581/1; EP/L006170/1
Type:
Published Article
Journal Name:
Chemistry of Materials
Additional Journal Information:
Journal Volume: 29; Journal Issue: 6; Journal ID: ISSN 0897-4756
Publisher:
American Chemical Society (ACS)
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Office of Science (SC). Basic Energy Sciences (BES) (SC-22); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
Language:
English
Subject:
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY
OSTI Identifier:
1348946
Alternate Identifier(s):
OSTI ID: 1361477

Pilania, Ghanshyam, Whittle, Karl R., Jiang, Chao, Grimes, Robin W., Stanek, Christopher Richard, Sickafus, Kurt E., and Uberuaga, Blas Pedro. Using machine learning to identify factors that govern amorphization of irradiated pyrochlores. United States: N. p., Web. doi:10.1021/acs.chemmater.6b04666.
Pilania, Ghanshyam, Whittle, Karl R., Jiang, Chao, Grimes, Robin W., Stanek, Christopher Richard, Sickafus, Kurt E., & Uberuaga, Blas Pedro. Using machine learning to identify factors that govern amorphization of irradiated pyrochlores. United States. doi:10.1021/acs.chemmater.6b04666.
Pilania, Ghanshyam, Whittle, Karl R., Jiang, Chao, Grimes, Robin W., Stanek, Christopher Richard, Sickafus, Kurt E., and Uberuaga, Blas Pedro. 2017. "Using machine learning to identify factors that govern amorphization of irradiated pyrochlores". United States. doi: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 Richard 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. Lastly, 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 = {2017},
month = {2}
}