Denoising Electron Densities from Stochastic Simulations of Electronic Structure Using ML скачать в хорошем качестве

Denoising Electron Densities from Stochastic Simulations of Electronic Structure Using ML

AI Winter School 2025, hosted by the Center for the Fundamental Physics of the Universe, Brown University Department of Physics, January 6-9, 2026 (https://indico.physics.brown.edu/even...) One of the most computationally-efficient approaches for modeling solids that feature strong electron correlation is by using Quantum Monte Carlo techniques. These techniques are among the most accurate available, but importantly, scale gracefully with system size because of their use of random sampling. Nonetheless, this random sampling can make the measurement of observables such as the electron charge or spin density noisy, requiring long averaging times. Here, we show how ML methods for the denoising of images can be used to denoise stochastic simulations of the electron density.