M. C. Sorkun

Sorkun, M. C. (2022). Artificial Intelligence-driven Discovery of Materials for Energy Applications (Eindhoven University of Technology). Eindhoven University of Technology, Eindhoven, Netherlands. Retrieved from https://research.tue.nl/nl/publications/artificial-intelligence-driven-discovery-of-materials-for-energy- (Original work published)

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)

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

Sorkun, M. C., Incel, O. D., & Paoli, C. . (2020). Time series forecasting on multi-variate solar radiation data using deep learning (LSTM). Turkish Journal of Electrical Engineering & Computer Sciences , 28, 211-223. https://doi.org/10.3906/elk-1907-218 (Original work published 2020)

Sorkun, M. C., Astruc, S. ., Koelman, J. M. V. A., & Er, S. . (2020). An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery. NPJ Computational Materials, 6(1), 106. https://doi.org/10.1038/s41524-020-00375-7 (Original work published 2020)

Sorkun, M. C., Khetan, A. ., & Er, S. . (2019). AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds. Nature Scientific Data, 6, 143. https://doi.org/10.1038/s41597-019-0151-1 (Original work published 2019)