- author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Matko Glucina and Jelena Musulin and Daniel Stifanic and Zlatan Car",
- title = "Utilization of Genetic Programming for Estimation of Molecular Structures Ground State Energies",
- booktitle = "1st Serbian International Conference on Applied Artificial Intelligence",
- year = "2022",
- address = "Kragujevac, Serbia",
- month = may # " 19-20",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, CHNOPS dataset, ground state energies",
-
URL = "
http://aai2022.kg.ac.rs/wp-content/uploads/upload/AAI_2022_Papers.zip",
- size = "4 pages",
- abstract = "GP to predict ground-state energies of molecules made up of C, H, N, O, P, and S (CHONPS) atoms. The GP was trained and tested on a publicly available dataset which consist of 16242 molecules where ground state energies were computed using the density functional theory (DFT). The optimal parameters of GP were chosen using the random parameter search method. After multiple GP executions, the best symbolic expression was chosen using a coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE). The best symbolic expression achieved R, MAE, and RMSE of 0.9434, 0.48, and 0.86, respectively.",
-
notes = "http://www.aai2022.kg.ac.rs/aai-2022-papers/
SICAAI proceedings published by Springer in 2023 doi:10.1007/978-3-031-29717-5",
