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.

机器学习正成为进行科学发现的重要工具。机器学习方法在材料开发领域的应用尤其吸引人，它可以通过发现新的更好的功能材料来实现创新。要将机器学习应用于实际的材料开发，科学家和机器学习工具之间必须紧密合作。但是，到目前为止，许多机器学习算法的黑匣子性质都阻碍了这种协作。从材料科学和物理学的角度来看，科学家通常很难解释数据驱动的模型。这里，我们通过使用一种可解释的机器学习方法，称为因式分解渐进贝叶斯推理专家分层混合系统（FAB / HME），展示了具有自旋Nernst效应的自旋驱动热电材料的发展。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以及有关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，从而确定了一种新型的自旋驱动热电材料。该材料显示了迄今为止最大的热电。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以及有关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，从而确定了一种新型的自旋驱动热电材料。该材料显示了迄今为止最大的热电。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以及有关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，从而确定了一种新型的自旋驱动热电材料。该材料显示了迄今为止最大的热电。