Quantum Many-Body Problem Solver Using Artificial Neural Networks: Applications to Strongly Correlated Electron Systems and Quantitative Material Property Prediction

Machine learning, particularly artificial neural networks integrated into variational Monte Carlo methods, enables the creation of highly accurate quantum many-body solvers. This approach allows for quantitative prediction of material properties in strongly correlated electron systems from first principles, advancing our understanding of complex quantum phenomena and enabling materials design.