Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores
Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores
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DOI：
10.1021/acs.chemmater.6b04666
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发表时间：
2016-07
影响因子：
8.6
通讯作者：
G. Pilania;K. Whittle;Chao Jiang;R. Grimes;C. Stanek;K. Sickafus;B. Uberuaga
中科院分区：
文献类型：
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作者：
G. Pilania;K. Whittle;Chao Jiang;R. Grimes;C. Stanek;K. Sickafus;B. Uberuaga
结构-性能关系是一个关键的材料科学概念，使新材料的设计。在辐射环境中应用的材料的情况下，将辐射耐受性与材料的基本结构特征相关联使得能够发现材料。在这里，我们使用一个机器学习模型来检查的因素，管理非晶化电阻在复杂的氧化物烧绿石（A2 B2 O 7）在制度中，非晶化发生的结果，缺陷积累。我们研究的准确性预测的基础上阳离子半径和电负性，氧的位置参数，以及无序和非晶化的材料的能量。没有一个单独的因素足以预测非晶化阻力。我们发现，当考虑多个家族的高温超导体（具有不同的B阳离子）时，半径和电负性提供了最好的预测，但是当机器学习模型仅限于B = Ti高温超导体时，disorption的能量学...
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 disor...