3.Machine Learning
Decision Tree, Random Forest, Association Rule Mining,Neural Network and Deep Learning are employed in analysis of many databases including photocatalytic hydrogen production, solubility of ionic liquids, perovskite solar cells,high performance lithium-sulfur batteries, reproducibility, hysteresis and stability relations in perovskite solar cells etc.
Some References
- Kilic A., Odabaşı Ç., Yildirim R., Eroglu D. (2020) Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning. Chemical Engineering Journal. 390. https://doi.org/10.1016/j.cej.2020.124117.
- Odabası, C. Yildirim, R. (2020). Assessment of Reproducibility, Hysteresis and Stability Relations in Perovskite Solar Cells Using Machine Learning. Energy Technology. https://doi.org/10.1002/ente.201901449.
- Odabası, C., Yildirim, R. (2019). Machine learning analysis on stability of perovskite solar cells. Solar Energy Materials and Solar Cells. 110284. https://doi.org/10.1016/j.solmat.2019.110284.
- Jalal A. , Can E. , Keskin S. , Yildirim R. , Uzun, A. (2019). Selection rules for estimating the solubility of C4-hydrocarbons in imidazolium ionic liquids determined by machine-learning tools. Journal of Molecular Liquids. 284. https://doi.org/10.1016/j.molliq.2019.03.182.
- Odabası, C., Yildirim, R. (2018). Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools. Nano Energy. 56. https://doi.org/ 10.1016/j.nanoen.2018.11.069.
- Can E. 2015 Construction and analysis of a database for photocatalytic water splitting from the published articles M.S. Thesis, Dept. of Chemical Eng., Boğaziçi Univ . CHE 2015 C36 .
