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Overview of Bayesian Optimization in Materials Science

  • Daniel Packwood
Chapter
Part of the SpringerBriefs in the Mathematics of Materials book series (BRIEFSMAMA, volume 3)

Abstract

Like any other field of research, materials science involves a lot of trial and error: in the process of creating a new material or device, we will inevitably make several prototypes which fail to perform as hoped.

References

  1. 1.
    Quote by President Obama, June 2011 at Carnegie Mellon University. See https://www.obamawhitehouse.archives.gov/mgi for more details.
  2. 2.
    Hinuma Y, et al. Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis. Nat Commun. 2016;7:11962.CrossRefGoogle Scholar
  3. 3.
    Seko A, et al. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys Rev Lett. 2015;115:205901.CrossRefGoogle Scholar
  4. 4.
    Seko A, et al. Machine learning with systematic density-functional theory calculations: application to melting temperatures of single- and binary-component solids. Phys Rev B. 2014;89:054303.CrossRefGoogle Scholar
  5. 5.
    Balachandran PV, et al. Adaptive strategies for materials design using uncertainties. Sci Rep. 2016;6:19660.CrossRefGoogle Scholar
  6. 6.
    Kiyohara S, et al. Acceleration of stable interface structure searching using a Kriging approach. Jpn J Appl Phys. 2016;55:045502.CrossRefGoogle Scholar
  7. 7.
    Ju S, et al. Designing nanostructures for photon transport via Bayesian optimization. Phys Rev X. 2017;7:021024.Google Scholar
  8. 8.
    Ueno T, et al. COMBO: An efficient Bayesian optimization library for materials science. Mater Discov. 2016;4:18.CrossRefGoogle Scholar
  9. 9.
    Rupp M, et al. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108:058301.CrossRefGoogle Scholar
  10. 10.
    Hansen K, et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J Chem Theory Comput. 2013;9:3404.CrossRefGoogle Scholar
  11. 11.
    Huo, H, Rupp, M. Unified representation for machine learning of molecules and crystals. arXiv:1704.06439v1 [physics.chem-ph].

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Institute for Integrated Cell-Materials Sciences (iCeMS)Kyoto UniversityKyotoJapan

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