In recent years, there has been a large effort in the materials science community to employ materials informatics to accelerate materials discovery or to develop new understanding of materials behavior. Materials informatics methods utilize machine learning techniques to extract new knowledge or predictive models out of existing materials data. In this review, we discuss major advances in the intersection between data science and atom-scale calculations with a particular focus on studies of solid-state, inorganic materials. The examples discussed in this review cover methods for accelerating the calculation of computationally-expensive properties, identifying promising regions for materials discovery based on existing data, and extracting chemical intuition automatically from datasets. We also identify key issues in this field, such as limited distribution of software necessary to utilize these techniques, and opportunities for areas of research that would help lead to the wider adoption of materials informatics in the atomistic calculations community.

近年来，材料科学界做出了巨大的努力，以利用材料信息学来加速材料发现或发展对材料行为的新理解。材料信息学方法利用机器学习技术从现有材料数据中提取新知识或预测模型。在这篇综述中，我们讨论了数据科学与原子尺度计算之间的交集方面的重大进展，特别着重于固态无机材料的研究。本文中讨论的示例涵盖了以下方法：加速计算昂贵的属性的计算，基于现有数据识别有希望发现材料的区域以及从数据集中自动提取化学直觉的方法。我们还确定了该领域的关键问题，