Materials and Megabytes

Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut
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Materials and Megabytes

Exploring the development of machine learning for materials science, physics, and chemistry applications through conversation with researchers at the forefront of this growing interdisciplinary field. Brought to you in collaboration by the Stanford Materials Computation and Theory Group and Qian Yang's lab at the University of Connecticut.

Episódios

  1. 13/01/2020

    Paper Interview - Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

    We discuss the paper Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models with the author Prof. Heather J. Kulik.Papers discussed in this episode:(Main discussion) Duan, C.; Janet, J. P.; Liu, F.; Nandy, A.; Kulik, H. J. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. J. Chem. Theory Comput. 2019, 15 (4), 2331–2345. https://doi.org/10.1021/acs.jctc.9b00057.(More on uncertainty metrics...

    23min

  2. 14/09/2019

    Paper interview - Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data

    We discuss the paper Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data with the authors Dr. Ekin Dogus Cubuk and Dr. Austin D. Sendek.Papers discussed in the episode:Cubuk, E. D.; Sendek, A. D.; Reed, E. J. Screening Billions of Candidates for Solid Lithium-Ion Conductors: A Transfer Learning Approach for Small Data. J. Chem. Phys. 2019, 150 (21), 214701. https://doi.org/10.1063/1.5093220.Sendek, A. D.; Yang, Q.; D. Cubuk, E.; N. Du...

    24min

  3. 09/04/2019

    Turab Lookman (Season 2, Ep.4)

    Our guest on this episode is Dr. Turab Lookman from Los Alamos National Laboratory. The interview took place at the 2018 MRS Fall meeting.Relevant papers:Gubernatis, J. E.; Lookman, T., Machine Learning in Materials Design and Discovery: Examples from the Present and Suggestions for the Future. Phys. Rev. Materials 2018, 2 (12), 120301. https://doi.org/10.1103/PhysRevMaterials.2.120301.Rickman, J. M.; Lookman, T.; Kalinin, S. V., Materials Informatics: From the Atomic-Level to the Continuum. ...

    19min

  4. 15/02/2019

    Patrick Riley (Season 2, Ep. 3)

    Our guest on this episode is Dr. Patrick Riley from Google Accelerated Science.Some relevant papers and links:Riley, P., Practical advice for analysis of large, complex data sets. The Unofficial Google Data Science Blog, www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html (2016)Zinkevich, M., Rules of Machine Learning: Best Practices for ML Engineering. https://developers.google.com/machine-learning/guides/rules-of-ml/ (last updated Oct 2018)Wigner, E....

    24min

  5. 25/01/2019

    O. Anatole von Lilienfeld (Season 2, Ep. 2)

    Our guest for this episode is Prof. Dr. O. Anatole von Lilienfeld from the University of Basel.Some relevant papers:Huang, B., and von Lilienfeld, O. A., The ‘DNA’ of Chemistry: Scalable Quantum Machine Learning with ‘Amons.’ arXiv:1707.04146, (2017)Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A., Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. Journal of Chemical Theory and Computation, doi:10.1021/acs.jctc.5b00099 (2015)Rupp, M....

    23min

  6. 24/01/2019

    Gábor Csányi (Season 2, Ep. 1)

    Our guest on this episode is Professor Gábor Csányi from the University of Cambridge.Some relevant papers:Bartok, A. P., Payne, M. C., Kondor, R., and Csanyi, G., Gaussianapproximation potentials: the accuracy of quantum mechanics, without the electrons. Physical Review Letters, doi:10.1103/PhysRevLett.104.136403(2010)Bartok, A. P., Kondor, R., and Csanyi, G., On representing chemical environments. Phys. Rev. B, doi:10.1103/PhysRevB.87.184115 (2013)Braams, B. J., and Bowman, J. M., Permutatio...

    40min

  7. 03/07/2018

    Evan J. Reed (Season 1, Ep. 3)

    Our guest on this episode is Professor Evan J. Reed from Stanford University.

    20min

  8. 02/07/2018

    Ekin Dogus Cubuk (Season 1, Ep. 2)

    Our guest on this episode is Dr. Ekin Doğuş Çubuk from Google Brain.

    28min

  9. 01/07/2018

    Kieron Burke (Season 1, Ep. 1)

    Our guest on this episode is Professor Kieron Burke from the University of California, Irvine.

    14min

  10. 30/06/2018

    Introduction (Season 1, Ep. 0)

    Start here for a brief introduction to this podcast!

    2min
  • 10 episódios

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Sobre

Exploring the development of machine learning for materials science, physics, and chemistry applications through conversation with researchers at the forefront of this growing interdisciplinary field. Brought to you in collaboration by the Stanford Materials Computation and Theory Group and Qian Yang's lab at the University of Connecticut.

Informações

  • Criado por
    Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut
  • Anos de atividade
    2019 - 2020
  • Episódios
    10
  • Classificação
    Livre
  • Copyright
    © 2020 Materials and Megabytes
  • Site do podcast
    Materials and Megabytes

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