Data-centric science for materials innovation
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Data-intensive scientific discovery The challenges of dealing with the rapid growth of data in materials science-related fields has long been recognized.1–3 With more recent advances in computer science, the tools for advancing data-intensive scientific discovery have opened the door for more engagement from the scientific community. As suggested by Gray, this has created “The Fourth Paradigm: Data-Intensive Scientific Discovery.”4 He pointed out that experimental, theoretical, and computational science were all being affected by the data deluge, and a fourth “data-intensive” science paradigm was emerging. Indeed, we are witnessing materials science being greatly affected in the new era of “datacentric” materials science, which will likely become the new paradigm for materials research and education. For more than a decade, MRS Bulletin has published issues related to the nexus of data science and materials science, including materials informatics5 and microstructural informatics.6 In this issue, we continue to expand on those themes by focusing on the numerous efforts in developing and utilizing databases of electronic structure calculations, and their impact on addressing different classes of problems in materials science.
Computational high-throughput screening First-principles calculations with predictive performance play an essential role in data-centric materials science. In 1990s,
researchers were able to make first-principles calculations of 10–100 inorganic crystalline compounds at most with less than a few atoms in a unit cell with a level of accuracy comparable to experiments. Density functional theory (DFT) is a reasonable way to fulfill the accuracy level without prohibitive computational costs. Today, developments of computational hardware and software have enabled computations of 105–106 compounds having much larger unit cells. These results have been stored in databases such as the Materials Project (MP) (materialsproject.org), AFLOW (aflowlib.org), OQMD (oqmd. org), NOMAD (www.nomad-coe.eu), and Materials Cloud (www.materialscloud.org). In order to construct such databases, powerful software tools to automate computational engines to run thousands of simulations are essential, as are application programming interfaces (APIs) for the resulting databases. Complex sequences of calculations are encoded into scientific workflows. Robust tools to store, search, and disseminate big data are important as well, and scientists benefit greatly from them. Such software platforms are described in this issue.7–11 When a target property can be accurately computed by DFT without excessive computational cost, high-throughput screening (HTS) within the DFT database is a straightforward strategy. These types of screening approaches have been used to design and discover materials with a wide range of
Isao Tanaka, Department of Materials Science and Engineering, and Elements Strategy Initiative for Structural Materials of Kyoto University, Japan; [email protected] Krishna Rajan, Department of Materials De
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