Representation of materials by kernel mean embedding
Representation of materials by kernel mean embedding
复制标题
通过核均值嵌入表示材料
DOI：
10.1103/physrevb.108.134107
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发表时间：
2023
影响因子：
3.7
通讯作者：
Ryo Yoshida
中科院分区：
文献类型：
--
作者：
Minoru Kusaba;Yoshihiro Hayashi;Chang Liu;Araki Wakiuchi;Ryo Yoshida
对于使用机器学习来预测材料属性，赋予模型的材料特征表示起着基础性的作用。一个模型描述材料属性作为任何给定的材料系统的函数，表示为一个固定长度的数字向量，通常称为描述符。然而，在大多数情况下，感兴趣的变量对于将它们的组成或结构特征（例如分子、晶体系统、化学组成和复合材料）编码到固定长度的向量中是不平凡的。传统上，为了将这样的多部件系统转换成固定长度的向量，预定义的部件特征的分布被汇总成几个汇总统计。这种简化操作的缺点是在矢量化过程中丢失了一些分布信息，如多峰性。在这里，我们提出了一个一般类的材料描述符的机器学习理论的内核均值嵌入的动机。与传统描述符不同，核均值嵌入可以保留向量化过程中分量特征分布的所有信息。此外，核平均描述符唯一地确定到原始材料空间的逆映射。我们展示了核平均描述符在各种应用中的表达能力和多功能性，包括预测无机化合物的形成能，预测形成准晶材料的化学组成，以及使用力场参数来表征聚合物材料。
For using machine learning to predict material properties, the feature representation of the materials given to the model plays a fundamental role. A model describes material properties as a function of any given material system expressed as a fixed-length numeric vector, often called a descriptor. However, in most cases, the variables of interest are nontrivial for encoding their compositional or structural features, such as molecules, crystal systems, chemical compositions, and composite materials, into a fixed-length vector. Conventionally, to translate such a multicomponent system into a fixed-length vector, the distribution of predefined component features is summarized into a few summary statistics. The disadvantage of this reduction operation is that some distributional information, such as multimodality, is lost in the vectorization process. Here, we present a general class of material descriptors motivated by the machine-learning theory of kernel mean embedding. Unlike conventional descriptors, kernel mean embedding can retain all information regarding the distribution of component features in the vectorization process. Furthermore, the kernel mean descriptor uniquely determines the inverse map to the original material space. We demonstrate the expressive power and versatility of the kernel mean descriptor in various applications, including the prediction of the formation energy of inorganic compounds, prediction of the chemical composition to form quasicrystalline materials, and the use of force-field parameters to characterize polymeric materials.
影响因子：
6.1
作者：
Jain, Anubhav;Shyue Ping Ong;Persson, Kristin A.
通讯作者：
Persson, Kristin A.