Abstract
Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.
| Original language | English |
|---|---|
| Pages (from-to) | 973-980 |
| Number of pages | 8 |
| Journal | Nature Materials |
| Volume | 14 |
| Issue number | 10 |
| DOIs | |
| State | Published - Sep 23 2015 |
Funding
The authors thank A. Borisevich, H. Christen, J. Morris, and D. Levy, as well as multiple colleagues at ORNL and elsewhere for valuable discussions. R.K.A. acknowledges The Compute and Data Environment (CADES) for continuous support. E. Strelcov and R. Vasudevan are gratefully acknowledged for help with figure preparation. Research was sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy. A portion of this research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. The algorithmic aspects were sponsored by the applied mathematics program at the DOE and the computational aspects made use of the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility at ORNL supported under contract no. DE-AC05-00OR22725.