6 papers
Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo
P. Bernát Szabó, Zeno Schätzle, Frank Noé
A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated…
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Adam Foster, Zeno Schätzle, P. Bernát Szabó +7
Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireferential character of the electronic structure in dissociating species. Mul…
Partitioning the electronic wave function using deep variational Monte Carlo
MatÄj Mezera, Paolo A. Erdman, Zeno Schätzle +2
We propose a novel wave function partitioning method that integrates deep-learning variational Monte Carlo with ansätze based on generalized product functions. This approach effec…
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
Zeno Schätzle, P. Bernát Szabó, Alice Cuzzocrea +2
The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods. When entire ground and excited potential energ…
Deep quantum Monte Carlo approach for polaritonic chemistry
Yifan Tang, Gian Marcello Andolina, Alica Cuzzocrea +5
Recent years have witnessed a surge of experimental and theoretical interest in controlling the properties of matter, such as its chemical reactivity, by confining it in optical ca…
Highly Accurate Real-space Electron Densities with Neural Networks
Lixue Cheng, P. Bernát Szabó, Zeno Schätzle +7
Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extractio…