The Materials Genome Initiative and artificial intelligence

被引:33
作者
Warren, James A. [1 ]
机构
[1] NIST, Mat Genome Program, Gaithersburg, MD 20899 USA
关键词
functional; government interactions; simulation; structural;
D O I
10.1557/mrs.2018.122
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The Materials Genome Initiative (MGI) seeks to accelerate the discovery, design, development, and deployment of new materials through the creation of a materials innovation infrastructure. This infrastructure is essentially a system for providing data and tools that encapsulate our existing knowledge about materials, and the means to create new knowledge. Given this approach, MGI is also deeply linked to the ongoing exponential growth in applications of machine learning and artificial intelligence (AI) to materials research. This article explores the connections between MGI, the consequent need for data publication, the implications for data-driven science, and the application of AI to materials design. Examples will demonstrate how materials research is transforming in remarkable ways, and that the MGI vision of accelerated materials discovery is within reach.
引用
收藏
页码:452 / 457
页数:6
相关论文
共 11 条
  • [1] Observation of Gravitational Waves from a Binary Black Hole Merger
    Abbott, B. P.
    Abbott, R.
    Abbott, T. D.
    Abernathy, M. R.
    Acernese, F.
    Ackley, K.
    Adams, C.
    Adams, T.
    Addesso, P.
    Adhikari, R. X.
    Adya, V. B.
    Affeldt, C.
    Agathos, M.
    Agatsuma, K.
    Aggarwal, N.
    Aguiar, O. D.
    Aiello, L.
    Ain, A.
    Ajith, P.
    Allen, B.
    Allocca, A.
    Altin, P. A.
    Anderson, S. B.
    Anderson, W. G.
    Arai, K.
    Arain, M. A.
    Araya, M. C.
    Arceneaux, C. C.
    Areeda, J. S.
    Arnaud, N.
    Arun, K. G.
    Ascenzi, S.
    Ashton, G.
    Ast, M.
    Aston, S. M.
    Astone, P.
    Aufmuth, P.
    Aulbert, C.
    Babak, S.
    Bacon, P.
    Bader, M. K. M.
    Baker, P. T.
    Baldaccini, F.
    Ballardin, G.
    Ballmer, S. W.
    Barayoga, J. C.
    Barclay, S. E.
    Barish, B. C.
    Barker, D.
    Barone, F.
    [J]. PHYSICAL REVIEW LETTERS, 2016, 116 (06)
  • [2] [Anonymous], INTEGRATED COMPUTATI
  • [3] [Anonymous], IN PRESS
  • [4] [Anonymous], DESIGNING REALITY BA
  • [5] [Anonymous], 2016, JOM, DOI DOI 10.1007/S11837-016-2226-1
  • [6] Machine Learning Force Fields: Construction, Validation, and Outlook
    Botu, V.
    Batra, R.
    Chapman, J.
    Ramprasad, R.
    [J]. JOURNAL OF PHYSICAL CHEMISTRY C, 2017, 121 (01) : 511 - 522
  • [7] Perspective: Composition-structure-property mapping in high-throughput experiments: Turning data into knowledge
    Hattrick-Simpers, Jason R.
    Gregoire, John M.
    Kusne, A. Gilad
    [J]. APL MATERIALS, 2016, 4 (05):
  • [8] Computational design of hierarchically structured materials
    Olson, GB
    [J]. SCIENCE, 1997, 277 (5330) : 1237 - 1242
  • [9] The Minerals Metals & Materials Society (TMS), 2017, Building a Materials Data Infrastructure: Opening New Pathways to Discovery and Innovation in Science and Engineering
  • [10] Making materials science and engineering data more valuable research products
    Ward, Charles H.
    Warren, James A.
    Hanisch, Robert J.
    [J]. INTEGRATING MATERIALS AND MANUFACTURING INNOVATION, 2014, 3 (01) : 292 - 308