The combination of multiple-principal element materials, known as high-entropy materials (HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is impossible to afford a holistic approach to explore each possibility. With the advance of the materials genome initiative and characterization technology, a high-throughput (HT) approach is more reasonable, especially to identify the specified functions for the new HEMs development. There are three major components for the HT approach, which are the computational tools, experimental tools, and digital data. This article reviews both the materials informatics and experimental approaches for the HT methods. Applications of these tools on composition-varying samples can be used to obtain stoichiometry effectively and phase-structure-property relationships efficiently for the materials-property database establishment. They can also be used in conjunction with machine learning (ML) to improve the predictability of models. These ML tools will be an essential part of HT approaches to develop the new HEMs. The ML-developed HEMs together with ML-created other materials are positioned in this manuscript for future HEMs advancement. Comparing all the reviewed properties, the hierarchical microstructures together with the heterogeneous grain sizes show the highest potential to apply ML for new HEMs, which needs HT validations to accelerate the development. The promising potential and the database from the HEMs exploration would shed light on the future of humanity building from the scratch of Mars regolith.

多主元材料的组合，称为高熵材料 (HEM)，将多维组成空间扩展到巨大的化学计量。不可能提供一种全面的方法来探索每种可能性。随着材料基因组计划和表征技术的进步，高通量（HT）方法更加合理，特别是为新的 HEM 开发确定特定功能。HT 方法有三个主要组成部分，即计算工具、实验工具和数字数据。本文回顾了 HT 方法的材料信息学和实验方法。这些工具在成分变化样品上的应用可用于有效地获得化学计量和相-结构-性质关系，以建立材料-性质数据库。它们还可以与机器学习 (ML) 结合使用，以提高模型的可预测性。这些 ML 工具将成为开发新 HEM 的 HT 方法的重要组成部分。ML 开发的 HEM 与 ML 创建的其他材料一起定位在本手稿中，用于未来 HEM 的发展。比较所有审查过的特性，分层微观结构和异质晶粒尺寸显示出将 ML 应用于新 HEM 的最大潜力，这需要 HT 验证以加速开发。