18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Abstract

          Data mining has revolutionized sectors as diverse as pharmaceutical drug discovery, finance, medicine, and marketing, and has the potential to similarly advance materials science. In this paper, we describe advances in simulation-based materials databases, open-source software tools, and machine learning algorithms that are converging to create new opportunities for materials informatics. We discuss the data mining techniques of exploratory data analysis, clustering, linear models, kernel ridge regression, tree-based regression, and recommendation engines. We present these techniques in the context of several materials application areas, including compound prediction, Li-ion battery design, piezoelectric materials, photocatalysts, and thermoelectric materials. Finally, we demonstrate how new data and tools are making it easier and more accessible than ever to perform data mining through a new analysis that learns trends in the valence and conduction band character of compounds in the Materials Project database using data on over 2500 compounds.

          Related collections

          Most cited references 77

          • Record: found
          • Abstract: found
          • Article: not found

          Survey of clustering algorithms.

           Rui Xu,  Donald Wunsch (2005)
          Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Bond-valence parameters for solids

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              P-type electrical conduction in transparent thin films of CuAlO2

                Bookmark

                Author and article information

                Journal
                applab
                Journal of Materials Research
                J. Mater. Res.
                Cambridge University Press (CUP)
                0884-2914
                2044-5326
                April 28 2016
                April 1 2016
                : 31
                : 08
                : 977-994
                Article
                10.1557/jmr.2016.80
                416b41ea-2493-4a20-b7b9-fec43578fe81
                © 2016

                Comments

                Comment on this article