Data Centric Design: A New Approach to Design of Microstructural Material Systems

Wei Chen*, Akshay Iyer, Ramin Bostanabad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)89-98
Number of pages10
StatePublished - Mar 2022


  • Bayesian optimization
  • Dimension reduction
  • Machine learning
  • Materials design
  • Materials informatics
  • Microstructure
  • Mixed-variable modeling
  • Reconstruction

ASJC Scopus subject areas

  • Computer Science(all)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Materials Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Engineering(all)


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