Many Electron Schr\"odinger Equation

Solving the many-electron Schrödinger equation, which describes the behavior of electrons in atoms and molecules, is a fundamental challenge in physics and chemistry. Current research focuses on developing accurate and efficient variational methods, employing neural networks (like FermiNet, PauliNet, and novel architectures using Pfaffians and self-attention) within Quantum Monte Carlo frameworks to approximate solutions. These advancements leverage transfer learning and improved optimization techniques (e.g., Wasserstein gradient flows) to reduce computational costs and improve accuracy, particularly for larger systems and time-dependent problems. This progress enables more precise predictions of molecular properties and material behaviors, impacting fields ranging from quantum chemistry to materials science.