Rational materials design based on prior knowledge is attractive because it promises to avoid time-consuming synthesis and testing of numerous materials candidates. However with the increase of complexity of materials, the scientific ability for the rational materials design becomes progressively limited. As a result of this complexity, combinatorial and high-throughput (CHT) experimentation in materials science has been recognized as a new scientific approach to generate new knowledge. This review demonstrates the broad applicability of CHT experimentation technologies in discovery and optimization of new materials. We discuss general principles of CHT materials screening, followed by the detailed discussion of high-throughput materials characterization approaches, advances in data analysis/mining, and new materials developments facilitated by CHT experimentation. We critically analyze results of materials development in the areas most impacted by the CHT approaches, such as catalysis, electronic and functional materials, polymer-based industrial coatings, sensing materials, and biomaterials.

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The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop "Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers" to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

Materials informatics provides the foundations for a new paradigm of materials discovery. It shifts our emphasis from one of solely searching among large volumes of data that may be generated by experiment or computation to one of targeted materials discovery via high-throughput identification of the key factors (i.e., "genes") and via showing how these factors can be quantitatively integrated by statistical learning methods into design rules (i.e., "gene sequencing") governing targeted materials functionality. However, a critical challenge in discovering these materials genes is the difficulty in unraveling the complexity of the data associated with numerous factors including noise, uncertainty, and the complex diversity of data that one needs to consider (i.e., Big Data). In this article, we explore one aspect of materials informatics, namely how one can efficiently explore for new knowledge in regimes of structure-property space, especially when no reasonable selection pathways based on theory or clear trends in observations exist among an almost infinite set of possibilities. 153 Annu. Rev. Mater. Res. 2015.45:153-169. Downloaded from www.annualreviews.org Access provided by Yale University -Law Library on 07/04/15. For personal use only.