Big Data of Materials Science: Critical Role of the Descriptor
Big Data of Materials Science: Critical Role of the Descriptor
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DOI：
10.1103/physrevlett.114.105503
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发表时间：
2015-03-10
影响因子：
8.6
通讯作者：
Scheffler, Matthias
中科院分区：
文献类型：
--
作者：
Ghiringhelli, Luca M.;Vybiral, Jan;Scheffler, Matthias
迄今为止，材料属性或功能的统计学习从一个基本上无声的、没有挑战的步骤开始：选择一组描述性参数（称为描述符）。然而，当描述符和驱动机制之间的科学联系不清楚时，学习到的描述符-属性关系的因果关系是不确定的。因此，对新的有前景材料的可信预测、异常现象的识别和科学进步都是值得怀疑的。我们分析这个问题并定义合适的描述符的要求。对于一个经典的例子，闪锌矿或纤锌矿和岩盐半导体的能量差异，我们演示了如何系统地找到有意义的描述符。
Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.