Material discovery using quantum-inspired technologies: validating a band gap engineering approach for inorganic materials.

The article focuses on the development and validation of a band gap engineering approach for inorganic materials using quantum-inspired technologies. Specifically, it applies a hybrid optimization method combining factorization machines with Fujitsu's Quantum-inspired Computing Digital Annealer (DA) and genetic algorithms (GA), termed FM-DA&GA, to optimize element arrangements in diamond-structured carbon (C), silicon (Si), and germanium (Ge). This method demonstrated superior performance over random search and conventional GA in efficiently identifying target band gaps and stable structures, successfully reproducing the known 3C-SiC material and extending to multi-element systems with up to 54 atoms. The study highlights FM-DA&GA's potential for accelerating inorganic material discovery, particularly for complex systems with vast compositional possibilities such as high-entropy alloys and perovskites, within practical computational times.