Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores

Pilania, G, Whittle, K ORCID: 0000-0002-8000-0857, Jiang, C, Grimes, RW, Stanek, CR, Sickafus, KE and Uberuaga, BP (2017) Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores. Chemistry of Materials, 29 (6). pp. 2574-2583.

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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.

Item Type:	Article
Uncontrolled Keywords:	Energy, Cations, Machine learning, Materials Irradiation
Depositing User:	Symplectic Admin
Date Deposited:	07 Apr 2017 08:54
Last Modified:	19 Jan 2023 07:06
DOI:	10.1021/acs.chemmater.6b04666
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3006838

Available Versions of this Item

• Using Machine Learning to Identify Factors That Govern Amorphization of Irradiated Pyrochlores. (deposited 14 Feb 2017 08:16)
  • Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores. (deposited 07 Apr 2017 08:54) [Currently Displayed]