M
Muratahan Aykol
Researcher at Toyota
Publications - 76
Citations - 7435
Muratahan Aykol is an academic researcher from Toyota. The author has contributed to research in topics: Computer science & Lithium. The author has an hindex of 25, co-authored 69 publications receiving 4373 citations. Previous affiliations of Muratahan Aykol include Middle East Technical University & Northwestern University.
Papers
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Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
TL;DR: The Open Quantum Materials Database (OQMD) as mentioned in this paper contains over 200,000 DFT calculated crystal structures and will be freely available for public use at http://oqmd.org.
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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin,James E. Saal,Bryce Meredig,Alexander Thompson,Jeff W. Doak,Muratahan Aykol,Stephan Ruhl,Chris Wolverton +7 more
TL;DR: The Open Quantum Materials Database (OQMD) as discussed by the authors is a high-throughput database consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD).
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Data-driven prediction of battery cycle life before capacity degradation
Kristen A. Severson,Peter M. Attia,Norman Jin,Nicholas Perkins,Benben Jiang,Zi Yang,Michael H. Chen,Muratahan Aykol,Patrick Herring,Dimitrios Fraggedakis,Martin Z. Bazant,Stephen J. Harris,Stephen J. Harris,William C. Chueh,Richard D. Braatz +14 more
TL;DR: In this article, a machine learning method was used to predict battery lifetime before the onset of capacity degradation with high accuracy. But, the prediction often cannot be made unless a battery has already degraded significantly.
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Accelerating the discovery of materials for clean energy in the era of smart automation
Daniel P. Tabor,Loïc M. Roch,Semion K. Saikin,Christoph Kreisbeck,Dennis Sheberla,Joseph Montoya,Shyam Dwaraknath,Muratahan Aykol,Carlos Ortiz,Hermann Tribukait,Carlos Amador-Bedolla,Christoph J. Brabec,Benji Maruyama,Kristin A. Persson,Kristin A. Persson,Alán Aspuru-Guzik +15 more
TL;DR: It is envisioned that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.
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Closed-loop optimization of fast-charging protocols for batteries with machine learning.
Peter M. Attia,Aditya Grover,Norman Jin,Kristen A. Severson,Todor M. Markov,Yang-Hung Liao,Michael H. Chen,Bryan Cheong,Nicholas Perkins,Zi Yang,Patrick Herring,Muratahan Aykol,Stephen J. Harris,Stephen J. Harris,Richard D. Braatz,Stefano Ermon,William C. Chueh,William C. Chueh +17 more
TL;DR: A closed-loop machine learning methodology of optimizing fast-charging protocols for lithium-ion batteries can identify high-lifetime charging protocols accurately and efficiently, considerably reducing the experimental time compared to simpler approaches.