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Data Analytics and What It Means to the Materials Community Proceedings of a Workshop (2021) / Chapter Skim
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6 Discussion
Pages 53-56

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
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From page 53...
... HOW WILL DATA, AI, AND DEEP LEARNING TRANSFORM MATERIALS DESIGN? Apurva Mehta, Stanford University, expressed his view that materials science is changing and posited that AI will have an increasingly larger role in those changes.
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From page 54...
... Bill Mahoney, ASM International, expressed his belief that data work is economically sustainable, pointing to the fact that the big data analytics market is worth $50 billion annually and growing fast, particularly with smaller, niche providers of independent data services. For its part, ASM is actively pursuing oppor­tunities to create a full suite of services supporting materials data analytics.
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From page 55...
... , Mehta suggested that a connected loop of sensors, analytics, and decision support via Bayesian processes or other statistical analyses could benefit materials science, but would require new infrastructure. Warren agreed that this idea holds potential, although there are benefits and drawbacks.
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Key Terms

  • materials design
  • analytics tools
  • materials data
  • warren noted
  • current manufacturing

  • benefit materials
  • government funding
  • building custom
  • laboratory results
  • apurva mehta

  • transform materials
  • driven primarily
  • custom technologies
  • government investment
  • coming decades

  • advancing materials
  • data services
  • maker spaces
  • successful laboratory
  • chemistry applications

  • change dramatically
  • mehta suggested
  • services supporting
  • wadley noted
  • sustain data

  • require collaboration
  • iot equipment
  • evaluate models
  • supporting materials
  • connected loop


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