Parts | Days | Selection | Search | Updates | Downloads | Help
AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz
AKPIK 2: Machine Learning & Physics
AKPIK 2.3: Talk
Wednesday, March 20, 2024, 15:30–15:45, MAR 0.002
Combining genetic algorithm and compressed sensing for features and operators selection in symbolic regression — •Aliaksei Mazheika1, Sergey V. Levchenko2, and Luca M. Ghiringhelli3,4 — 1Technische Universitaet Berlin, DE — 2Moscow, RU — 3The NOMAD Laboratory at the Fritz Haber Institute and Humboldt University, Berlin, DE — 4Friedrich-Alexander University, Erlangen, DE
The symbolic inference method SISSO (Sure-Independence Screening and Sparsifying Operator) has recently found a broad application in materials science. It performs regression or classification by adopting compressed sensing for the selection of an optimized subset of features and mathematical operators out of a given set of candidates. However, SISSO becomes computationally unpractical when the set of candidate features and operators exceeds the size of few tens. Here we combine SISSO with a genetic algorithm (GA) for the global search of the optimal subset of features and operators. We test GA-SISSO for the search of predictive models of perovskites lattice parameters, and demonstrate that our method efficiently finds more accurate models than the original SISSO. GA-SISSO was also applied for the search of the model for prediction of CO2 adsorption energies on semiconductor oxides. The model learned by GA-SISSO has much higher accuracy compared to previously discussed models based on the O 2p-band center. The statistical analysis of contributions of all features to the learned models shows that, besides the O 2p-band center, the electrostatic potential above adsorption sites and surface formation energies are key features.
Keywords: symbolic regression; perovskites; CO2; adsorption