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84b3b1c9419c486afbe5 · Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm

Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific

Официальный источник · 2020-07-13

Полные импортированные сведения карточки
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  "title_en": "Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2020-07-13",
  "date_original": "July 13, 2020",
  "url": "https://www.nist.gov/publications/scientific-ai-materials-science-path-sustainable-and-scalable-paradigm",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Brian DeCost , Jason Hattrick-Simpers , Zachary Trautt , Aaron Kusne , Martin L. Green , Eva Campo",
  "abstract_excerpt_en": "Recent years have seen an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific",
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      "title_en": "Scientific AI in Materials Science: a Path to a Sustainable and Scalable Paradigm",
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