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 perspective, 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 using a tightly integrated approach where theory and experiments are linked through big-deep data.

• Publication year

  2015

• Venue

  Unknown venue

• Publication date

  2015-11-25

• Fields of study

  Materials Science, Computer Science, Engineering

• Identifiers
  DOI 10.1038/NPJCOMPUMATS.2015.8
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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