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@article{Pilania:2017:10.1021/acs.chemmater.6b04666,
author = {Pilania, G and Whittle, KR and Jiang, C and Grimes, RW and Stanek, CR and Sickafus, KE and Uberuaga, BP},
doi = {10.1021/acs.chemmater.6b04666},
journal = {CHEMISTRY OF MATERIALS},
pages = {2574--2583},
title = {Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores},
url = {http://dx.doi.org/10.1021/acs.chemmater.6b04666},
volume = {29},
year = {2017}
}

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TY  - JOUR
AB  - 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.
AU  - Pilania,G
AU  - Whittle,KR
AU  - Jiang,C
AU  - Grimes,RW
AU  - Stanek,CR
AU  - Sickafus,KE
AU  - Uberuaga,BP
DO  - 10.1021/acs.chemmater.6b04666
EP  - 2583
PY  - 2017///
SN  - 0897-4756
SP  - 2574
TI  - Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores
T2  - CHEMISTRY OF MATERIALS
UR  - http://dx.doi.org/10.1021/acs.chemmater.6b04666
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000398014600022&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/46121
VL  - 29
ER  -

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