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Zhang, Q., Khetan, A., Sorkun, E., Niu, F., Loss, A., Pucher, I., & Er, S. (2022). Data-driven discovery of small electroactive molecules for energy storage in aqueous redox flow batteries. Energy Storage Materials, 47, 167-177. https://doi.org/10.1016/j.ensm.2022.02.013 (Original work published 2022)
Chang, F., Tezsevin, I., de Rijk, J., Meeldijk, J., Hofmann, J., Er, S., … de Jongh, P. (2022). Potassium hydride-intercalated graphite as an efficient heterogeneous catalyst for ammonia synthesis. Nature Catalysis, 5(3), 222–230. https://doi.org/10.1038/s41929-022-00754-x
Sorkun, M. C., Mullaj, D., Koelman, J. M. V. A., & Er, S. (2022). ChemPlot, a Python Library for Chemical Space Visualization. Chemistry-Methods, 2(7), e202200005. https://doi.org/10.1002/cmtd.202200039
Sorkun, E., Zhang, Q., Khetan, A., Sorkun, M. C., & Er, S. (2022). RedDB, a computational database of electroactive molecules for aqueous redox flow batteries. Nature Scientific Data, 9, 718. https://doi.org/10.1038/s41597-022-01832-2 (Original work published 2022)
Zhang, Q., Khetan, A., Sorkun, E., & Er, S. (2022). Discovery of aza-aromatic anolytes for aqueous redox flow batteries via high-throughput screening. Journal of Materials Chemistry A, 10(41), 22214-22227. https://doi.org/10.1039/D2TA05674G (Original work published 2022)
Tezsevin, I., van de Sanden, M. M., & Er, S. (2021). High-Throughput Computational Screening of Cubic Perovskites for Solid Oxide Fuel Cell Cathodes. Journal of Physical Chemistry Letters, 12(17), 4160-4165. https://doi.org/10.1021/acs.jpclett.1c00827 (Original work published 2021)
Liu, Z., Hussain, T., Karton, A., & Er, S. (2021). Empowering hydrogen storage properties of haeckelite monolayers via metal atom functionalization. Applied Surface Science, 556, 149709. https://doi.org/10.1016/j.apsusc.2021.149709 (Original work published 2021)
Zhang, Q., Khetan, A., & Er, S. (2021). A Quantitative Evaluation of Computational Methods to Accelerate the Study of Alloxazine-Derived Electroactive Compounds for Energy Storage. Scientific Reports, 11, 4089. https://doi.org/10.1038/s41598-021-83605-2 (Original work published 2021)
Sorkun, M. C., Koelman, J. M. V. A., & Er, S. (2021). Pushing the limits of solubility prediction via quality-oriented data selection. IScience, 24(1), 101961. https://doi.org/10.1016/j.isci.2020.101961