Identification of advanced spin-driven thermoelectric materials via interpretable machine learning
Identification of advanced spin-driven thermoelectric materials via interpretable machine learning
复制标题
DOI：
10.1038/s41524-019-0241-9
复制
发表时间：
2019-10-30
影响因子：
9.7
通讯作者：
Yorozu, Shinichi
中科院分区：
文献类型：
--
作者：
Iwasaki, Yuma;Sawada, Ryohto;Yorozu, Shinichi
机器学习正在成为科学发现的重要工具。特别有吸引力的是机器学习方法在材料开发领域的应用，它通过发现新的和更好的功能材料来实现创新。要将机器学习应用于实际材料开发，科学家和机器学习工具之间的密切合作是必要的。然而，到目前为止，这种合作一直受到许多机器学习算法的黑箱性质的阻碍。科学家通常很难从材料科学和物理学的角度解释数据驱动的模型。在这里，我们展示了自旋驱动热电材料的发展与异常能斯特效应，通过使用一种可解释的机器学习方法，称为因子化渐近贝叶斯推理分层专家混合（FAB/HME）。基于材料科学和物理学的先验知识，我们能够从可解释的机器学习中提取一些令人惊讶的相关性和关于自旋驱动热电材料的新知识。在此指导下，我们进行了实际的材料合成，导致识别一种新型的自旋驱动热电材料。这种材料显示出迄今为止最大的热电势。
Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date.