1 citations · 1 across the 2 of their papers we have counts for
3 papers
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
Michael Scherbela, Nicholas Gao, Philipp Grohs +1
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-networ…
Transferable Neural Wavefunctions for Solids
Leon Gerard, Michael Scherbela, Halvard Sutterud +2
Deep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as a highly accurate approach for finding approximate solutions to the many-electron Schrödinger equation.…
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions
Michael Scherbela, Leon Gerard, Philipp Grohs
Obtaining accurate solutions to the Schrödinger equation is the key challenge in computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) has recently…