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
Turab Lookman (Season 2, Ep.4)
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Gowoon Cheon / Turab Lookman
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Season 2
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Episode 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. Acta Materialia 2019, 168, 473–510. https://doi.org/10.1016/j.actamat.2019.01.051.
- Lookman, T.; Balachandran, P. V.; Xue, D.; Yuan, R. Active Learning in Materials Science with Emphasis on Adaptive Sampling Using Uncertainties for Targeted Design. npj Computational Materials 2019, 5 (1), 21. https://doi.org/10.1038/s41524-019-0153-8.
- Xue, D.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D.; Lookman, T., Accelerated Search for Materials with Targeted Properties by Adaptive Design. Nature Communications 2016, 7, 11241. https://doi.org/10.1038/ncomms11241.