Gaussian orbital expansions are optimized using artificial intelligence-based evolutionary computing for confined atomic and molecular systems. The method enables accurate Gaussian basis sets adapted to spatial confinement and extends naturally to solvated molecules via self-consistent reaction field models. This theoretical framework enhances orbital modeling under both physical and solvent-induced constraints.

Two artificial intelligence techniques, genetic algorithms and differential evolution, are applied to generate Gaussian expansions (φ-nG) for both free and sphere-confined atomic orbitals. A program (UCA-GSS-GA) is developed to enable the efficient calculation of these expansions. The accuracy of the obtained expansions is analyzed, and they are used to significantly refine self-consistent reaction field models for solvent effects. The orbitals, atoms, and molecules analyzed include some of the simplest systems (1s, H, H₂, and CH₄). However, the developed program is capable of handling all types of molecules and solvents.