Accelerating optimization of composite material formulations having best property through AI models and quasi-quantum computing methods

Abstract Integrating AI into materials science accelerates material discovery. This study develops an Ising model for composite material optimization using machine learning and solves it efficiently with an annealing computer. Unlike traditional methods limited to directly representable problems, our approach converts machine learning into Ising models. It includes a transformation unit to binarize variables, a training unit to learn an Ising model, and an output unit to optimize using the trained model. With an annealing computer, we achieve speeds tens of thousands of times faster than exhaustive methods. Compositional constraints are incorporated as penalty functions to ensure valid combinations. This novel method transforms machine learning predictions into Ising models, enabling rapid optimization and advancing AI applications in materials science. Graphical abstract