论文标题
用小组结构进行材料的机器学习建模
Machine Learning Modeling of Materials with a Group-Subgroup Structure
论文作者
论文摘要
通过连续相变连接的晶体结构通过晶体学组及其亚组之间的数学关系连接。在本研究中，我们介绍了群组 - 组机器学习（GS-ML），并表明在训练集中包括小单位细胞的材料会减少样本外预测误差，这些材料具有大型单元细胞的材料。与其他ML方法相比，GS-ML的培训成本最少达到目标准确性2-3％。由于可用的材料数据集是异质的，因此提供了不足的示例来实现小组结构，因此我们介绍了具有8393 Q1D有机金属材料的“ Friezermq1d”数据集，该数据集均匀分布在7个Frieze组中。此外，通过比较FCHL和1-HOT表示的性能，我们显示GS-ML在描述符编码结构信息时有效地捕获亚组信息。所提出的方法是通用的，可扩展到对称抽象（例如自旋，价值或电荷顺序）。
Crystal structures connected by continuous phase transitions are linked through mathematical relations between crystallographic groups and their subgroups. In the present study, we introduce group-subgroup machine learning (GS-ML) and show that including materials with small unit cells in the training set decreases out-of-sample prediction errors for materials with large unit cells. GS-ML incurs the least training cost to reach 2-3% target accuracy compared to other ML approaches. Since available materials datasets are heterogeneous providing insufficient examples for realizing the group-subgroup structure, we present the "FriezeRMQ1D" dataset with 8393 Q1D organometallic materials uniformly distributed across 7 frieze groups. Furthermore, by comparing the performances of FCHL and 1-hot representations, we show GS-ML to capture subgroup information efficiently when the descriptor encodes structural information. The proposed approach is generic and extendable to symmetry abstractions such as spin-, valency-, or charge order.
