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Title: A Bridge for Accelerating Materials by Design

Recent technical advances in the area of nanoscale imaging, spectroscopy, and scattering/diffraction have led to unprecedented capabilities for investigating materials structural, dynamical and functional characteristics. In addition, recent advances in computational algorithms and computer capacities that are orders of magnitude larger/faster have enabled large-scale simulations of materials properties starting with nothing but the identity of the atomic species and the basic principles of quantum- and statistical-mechanics and thermodynamics. Along with these advances, an explosion of high-resolution data has emerged. This confluence of capabilities and rise of big data offer grand opportunities for advancing materials sciences but also introduce several challenges. In this editorial we identify challenges impeding progress towards advancing materials by design (e.g., the design/discovery of materials with improved properties/performance), possible solutions, and provide examples of scientific issues that can be addressed by using a tightly integrated approach where theory and experiments are linked through big-deep data.
 [1] ;  [1] ;  [1] ;  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 1; Journal ID: ISSN 2057-3960
Nature Publishing Group
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING Big Data; Machine learning