Muratahan Aykol
Affiliations: | Toyota Research Institute |
Area:
computational materials science, DFT, batteries, machine learningGoogle:
"Muratahan Aykol"Mean distance: (not calculated yet)
Parents
Sign in to add mentor| Chris Wolverton | grad student | 2015 | Northwestern |
| Kristin Aslaug Ceder-Persson | post-doc | 2017 | LBNL |
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Publications
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| Wang C, Aoyagi K, Aykol M, et al. (2020) Ionic Conduction through Reaction Products at the Electrolyte-Electrode Interface in All-Solid-State Li Batteries. Acs Applied Materials & Interfaces |
| Hegde VI, Aykol M, Kirklin S, et al. (2020) The phase stability network of all inorganic materials. Science Advances. 6: eaay5606 |
| Attia PM, Grover A, Jin N, et al. (2020) Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature. 578: 397-402 |
| Montoya JH, Winther KT, Flores RA, et al. (2020) Autonomous intelligent agents for accelerated materials discovery Chemical Science. 11: 8517-8532 |
| Rohr B, Stein HS, Guevarra D, et al. (2020) Benchmarking the acceleration of materials discovery by sequential learning Chemical Science. 11: 2696-2706 |
| Flores RA, Paolucci C, Winther KT, et al. (2020) Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction Chemistry of Materials. 32: 5854-5863 |
| Sivonxay E, Aykol M, Persson KA. (2020) The lithiation process and Li diffusion in amorphous SiO2 and Si from first-principles Electrochimica Acta. 331: 135344-135344 |
| Aykol M, Hegde VI, Hung L, et al. (2019) Network analysis of synthesizable materials discovery. Nature Communications. 10: 2018 |
| Aykol M, Kim S, Hegde VI, et al. (2019) Computational evaluation of new lithium-3 garnets for lithium-ion battery applications as anodes, cathodes, and solid-state electrolytes Physical Review Materials. 3 |
| Severson KA, Attia PM, Jin N, et al. (2019) Data-driven prediction of battery cycle life before capacity degradation Nature Energy. 4: 383-391 |