Characteristics maximization through halogenation in hexa-peri-hexabenzocoronene utilizing quantum inspired technology

Abstract This article demonstrates a high-speed search for polycyclic aromatic hydrocarbons with desirable properties, combining Fujitsu's Quantum-inspired Computing Digital Annealer (DA) with density functional theory (DFT) calculations. The target compound is a nanographene based on hexa-peri-hexabenzocoronene (HBC), known for its wide π-conjugated plane, enabling high electron conductivity and molecular alignment. We optimized halogen substituents (F, Cl) in HBC to maximize dipole moment and minimize lowest unoccupied molecular orbital (LUMO), calculated by DFT. A bit representation was used for 18 hydrogen atom substitutions. Factorization Machines (FM) were employed for formulation, and optimization was performed using DA (FM-DA). However, its dependence on initial data led to the development of FM-DA&GA, incorporating Genetic Algorithm (GA) for parallel recommendations. FM-DA&GA effectively searched through 236 (approximately 68.7 billion) bit combinations, finding the optimal solution. These bit combinations correspond to 318 (approximately 387 million) chemical structures, if chemical symmetry is disregarded. The combined approach of DA's fast search on the FM model and GA's global search prevented local solution traps, enabling a more efficient and comprehensive exploration of the solution space. This synergistic effect represents a powerful tool for efficient material discovery, particularly in overcoming the limitations of traditional optimization methods.