<description>&lt;p&gt;We discuss the paper &lt;em&gt;Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data &lt;/em&gt;with the authors Dr. Ekin Dogus Cubuk and Dr. Austin D. Sendek.&lt;br/&gt;&lt;br/&gt;&lt;b&gt;Papers discussed in the episode:&lt;/b&gt;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;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. &lt;em&gt;J. Chem. Phys.&lt;/em&gt; &lt;b&gt;2019&lt;/b&gt;, &lt;em&gt;150&lt;/em&gt; (21), 214701. &lt;a href='https://doi.org/10.1063/1.5093220'&gt;https://doi.org/10.1063/1.5093220&lt;/a&gt;.&lt;/li&gt;&lt;li&gt;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. &lt;em&gt;Energy &amp;amp; Environmental Science&lt;/em&gt; &lt;b&gt;2017&lt;/b&gt;, &lt;em&gt;10&lt;/em&gt; (1), 306–320. &lt;a href='https://doi.org/10.1039/C6EE02697D'&gt;https://doi.org/10.1039/C6EE02697D&lt;/a&gt;.&lt;/li&gt;&lt;li&gt;Hinton, G.; Vinyals, O.; Dean, J. Distilling the Knowledge in a Neural Network. &lt;em&gt;arXiv:1503.02531 [cs, stat]&lt;/em&gt; &lt;b&gt;2015&lt;/b&gt;.&lt;/li&gt;&lt;li&gt;Zhou, Q.; Tang, P.; Liu, S.; Pan, J.; Yan, Q.; Zhang, S.-C. Learning Atoms for Materials Discovery. &lt;em&gt;PNAS&lt;/em&gt; &lt;b&gt;2018&lt;/b&gt;, &lt;em&gt;115&lt;/em&gt; (28), E6411–E6417. &lt;a href='https://doi.org/10.1073/pnas.1801181115'&gt;https://doi.org/10.1073/pnas.1801181115&lt;/a&gt;.&lt;/li&gt;&lt;li&gt;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. &lt;em&gt;arXiv:1904.08996 [cond-mat, physics:physics]&lt;/em&gt; &lt;b&gt;2019&lt;/b&gt;.&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;&lt;br/&gt;&lt;/p&gt;</description>

Materials and Megabytes

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

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

SEP 14, 201923 MIN
Materials and Megabytes

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

SEP 14, 201923 MIN

Description

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.