Aykol, Muratahan

Data-driven prediction of battery cycle life before capacity degradation

Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy

Battery Modeling and ManagementAdvancements in Battery Materials

Cited by 3,228

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013) · JOM

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,584

Scaling deep learning for materials discovery

Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 1,527

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature

Battery Modeling and ManagementMachine Learning in Materials Science

Cited by 1,180

Network analysis of synthesizable materials discovery

Aykol, M., Hegde, V. I., Hung, L., Suram, S., Herring, P., Wolverton, C., & Hummelshøj, J. S. (2019) · Nature Communications

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 120

Rational Solid-State Synthesis Routes for Inorganic Materials

Aykol, M., Montoya, J. H., & Hummelshøj, J. (2021) · Journal of the American Chemical Society

Machine Learning in Materials ScienceThermal and Kinetic Analysis

Cited by 117

Computational Discovery of Li-M-O Ion Exchange Materials for Lithium Extraction from Brines

Snydacker, D. H., Hegde, V. I., Aykol, M., & Wolverton, C. (2018) · Chemistry of Materials

Extraction and Separation ProcessesAdvancements in Battery Materials

Cited by 39