Abstract

The traditional aim of materials science is to establish the causal relationships between composition, processing, structure, and properties with the intention that, eventually, these relationships will make it possible to design materials to meet specifications. This paper explores another approach. If properties are related to structure at different scales, there may be relationships between properties that can be discerned and used to make predictions so that knowledge of some properties in a compositional field can be used to predict others. We use the physical properties of the elements as a dataset because it is expected to be both extensive and reliable and we explore this method by showing how it can be applied to predict the polarizability of the elements from other properties.

中文翻译：

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使用人工神经网络探索属性之间的相关性

抽象的

材料科学的传统目标是建立组成，加工，结构和特性之间的因果关系，以期最终使这些关系使设计符合规格的材料成为可能。本文探讨了另一种方法。如果属性与不同规模的结构相关，则可以识别并用于进行预测的属性之间可能存在关系，以便可以将组成字段中某些属性的知识用于预测其他属性。我们将元素的物理属性用作数据集，因为它被认为既广泛又可​​靠，并且我们将通过展示如何将其用于预测其他属性的元素的极化率来探索该方法。