在高通量计算材料科学时代，材料基因组计划的核心是为计算材料设计建立数据处理、材料结构和材料属性（PSP）之间的关系。近年来，在数据获取和存储，微结构表征和重建（MCR），机器学习（ML），材料建模和仿真，数据处理、材料制造和实验方面取得的技术进步，显著提升了研究人员在PSP关系的建立和逆向材料设计方面的能力。本文将从设计研究的角度审视这些进步。特别介绍了一种数据中心设计方法，并从本质上将该方法分为三个方面：设计表征、设计评估和设计合成。每个方面的发展都由领域知识指导并从中受益。因此，针对每个方面，提出了一种应用广泛的计算方法，这些方法的集成实现了以数据为中心的材料发现和设计。

Building processing, structure, and property (PSP) relations for computational materials design is at the heart of the Materials Genome Initiative in the era of high-throughput computational materials science. Recent technological advancements in data acquisition and storage, microstructure characterization and reconstruction (MCR), machine learning (ML), materials modeling and simulation, data processing, manufacturing, and experimentation have significantly advanced researchers' abilities in building PSP relations and inverse material design. In this article, we examine these advancements from the perspective of design research. In particular, we introduce a data-centric approach whose fundamental aspects fall into three categories: design representation, design evaluation, and design synthesis. Developments in each of these aspects are guided by and benefit from domain knowledge. Hence, for each aspect, we present a wide range of computational methods whose integration realizes data-centric materials discovery and design.