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