6 citations · 14 across the 6 of their papers we have counts for
6 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…
Towards a Foundation Model for Neural Network Wavefunctions
Michael Scherbela, Leon Gerard, Philipp Grohs
Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schrödinger equat…
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?
Leon Gerard, Michael Scherbela, Philipp Marquetand +1
Finding accurate solutions to the Schrödinger equation is the key unsolved challenge of computational chemistry. Given its importance for the development of new chemical compounds,…
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks
Michael Scherbela, Rafael Reisenhofer, Leon Gerard +2
Accurate numerical solutions for the Schrödinger equation are of utmost importance in quantum chemistry. However, the computational cost of current high-accuracy methods scales poo…