npj：先进热电材料的探寻—又是机器学习！

要将机器学习应用于实际的材料开发中，科学家和机器学习工具之间必须紧密协作。材料科家经常尝试理解数据驱动模型背后的原理，以便获得一些可操作的信息来指导材料开发。但是，到目前为止，由于许多机器学习方法的可解释性较差，此类尝试道阻且长，几乎山穷水尽。可解释性机器学习（可解释或透明的机器学习）想法（概念）应运而生，希望其不仅具有很高的预测能力，而且要有很高的可解释性。但究竟如何实现、如何与科学家之间协同作用，尚缺少实例。

本研究通过一种可进行物理解释的机器学习方法（FAB / HMEs：分解的渐进贝叶斯推理专家分层混合）来展示具有异常能斯特效应的自旋驱动的热电材料的开发。由日本NEC公司中央研究实验室的岩崎由马教授领导的团队，使用了最先进的可解释机器学习模型，成功地展示了FAB / HMEs与材料科学家之间的协同工作和材料的研发。由于模型具有较高的预测能力和可解释性，材料科学家可以从这些模型中获得对新材料开发有用的新知识。通过可解释模型得到的规律，作者成功地开发了一种自旋驱动的热电材料，其热电动势SSTE大于目前的热电材料。此外，他们从数据驱动模型中发现的新见解也可以使他们更全面地理解自旋驱动热电材料背后的机理。因此，可解释性的机器学习模型不仅可以帮助全新材料的开发，而且可以指导相关理论的研究。

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.