Materials and Megabytes

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

September 14, 2019 Season 3 Episode 1
Materials and Megabytes
Paper interview - Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
Chapters
Materials and Megabytes
Paper interview - Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
Sep 14, 2019 Season 3 Episode 1
Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut
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.
Show Notes

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. Duerloo, K.-A.; Cui, Y.; J. Reed, E. Holistic Computational Structure Screening of More than 12000 Candidates for Solid Lithium-Ion Conductor Materials. Energy & Environmental Science 2017, 10 (1), 306–320. https://doi.org/10.1039/C6EE02697D.
  • Hinton, G.; Vinyals, O.; Dean, J. Distilling the Knowledge in a Neural Network. arXiv:1503.02531 [cs, stat] 2015.
  • Zhou, Q.; Tang, P.; Liu, S.; Pan, J.; Yan, Q.; Zhang, S.-C. Learning Atoms for Materials Discovery. PNAS 2018, 115 (28), E6411–E6417. https://doi.org/10.1073/pnas.1801181115.
  • Sendek, A. D.; Cheon, G.; Pasta, M.; Reed, E. J. Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective. arXiv:1904.08996 [cond-mat, physics:physics] 2019.




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