In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.

Methods that simulate electron-level mechanisms on supercomputers are widely used to explore novel materials and understand biological phenomena. There is strong demand for new approaches that can deliver faster predictions while maintaining high accuracy.