Muratahan Aykol - Concepedia

Author

Muratahan Aykol

Also Known As

Aykol, Muratahan, Aykol, Muratahan; Kim, Soo; Hao, Shiqiang; Lu, Zhi; Hegde, Vinay Ishwar; Snydacker, David H.; Kirklin, Scott J.; Morgan, Dane; Wolverton, Christopher M., Huck, Patrick, M. Aykol, Muratahan Aykol, Saurabh Bajaj

136

Publications

14.8K

Citations

H-Index

106

Concepts

All Affiliations

Toyota Research Institute Northwestern University Lawrence Berkeley National Laboratory Middle East Technical University Toyota Industries (United States)

About

Muratahan Aykol is an author at Toyota Research Institute specializing in engineering, materials science, and chemistry.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	23	29	10.3K
2	Natural Sciences	5	5	4.7K
3	Business	2	2	54
4	Education	1	1	31
5	Medicine	1	1	90

Muratahan Aykol

Publications

13.4K

Citations

H-Index

Page 1

	Year	Citations
Data-driven prediction of battery cycle life before capacity degradation Kristen Severson, Peter M. Attia, Norman Jin, Nature Energy EngineeringData ScienceLife PredictionEnergy StorageLife Cycle Assessment	2019	2.6K
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) James E. Saal, Scott Kirklin, Muratahan Aykol, JOM Materials ScienceQuantum ScienceEngineeringPhysicsNatural Sciences	2013	2.4K
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Scott Kirklin, James E. Saal, Bryce Meredig, npj Computational Materials EngineeringComputational ChemistryChemistryElectronic StructureSpectra-structure Correlation	2015	2.2K
Scaling deep learning for materials discovery Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Nature	2023	984
Closed-loop optimization of fast-charging protocols for batteries with machine learning Peter M. Attia, Aditya Grover, Norman Jin, Nature Electric BatteryElectrical EngineeringEngineeringMachine LearningEnergy Management	2020	971
Thermodynamic limit for synthesis of metastable inorganic materials Muratahan Aykol, Shyam Dwaraknath, Wenhao Sun, Science Advances	2018	375
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows Kiran Mathew, Joseph H. Montoya, Alireza Faghaninia, Computational Materials Science Materials ScienceComputational ScienceEngineeringScientific Workflow SystemHigh-level Interface	2017	357
A machine learning approach for engineering bulk metallic glass alloys Logan Ward, Stephanie Chan O׳Keeffe, Joseph Stevick, Acta Materialia Materials ScienceEngineeringMachine LearningPhysic Aware Machine LearningMachine Learning Approach	2018	238
Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime Muratahan Aykol, Chirranjeevi Balaji Gopal, Abraham Anapolsky, Journal of The Electrochemical Society EngineeringMachine LearningLife PredictionMachine Learning ToolMachine Learning Models	2021	229
High-throughput computational design of cathode coatings for Li-ion batteries Muratahan Aykol, Soo Kim, Vinay I. Hegde, Nature Communications	2016	202

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