Deep-learning electronic structure calculations

First-principles electronic structure calculations face a fundamental accuracy–efficiency dilemma: highly accurate quantum Monte Carlo methods are too expensive for large systems, while efficient DFT sacrifices precision. This review surveys two emerging deep-learning paradigms that are breaking through this bottleneck: deep-learning quantum Monte Carlo (DL-QMC) achieves unprecedented accuracy by using neural networks as many-body wavefunction ansatzes, while deep-learning DFT (DL-DFT) enables million-atom simulations with ab initio quality. We discuss the key methodological advances and envision how the synergy of these approaches may reshape the future of computational materials science.