10 papers · 1 filter
Benchmark of Multi-Channel Dyson Equation and Algebraic Diagrammatic Construction Methods for molecules
Mike Keizer, Stefano Paggi, J. Arjan Berger +2
The Dyson-algebraic diagrammatic construction (ADC) and the multi-channel Dyson equation (MCDE) formalisms explicitly leverage multi-particle channels to formulate correlated theor…
Approaching Coupled Cluster Accuracy with Positive Semidefinite Vertex Corrected Self-Energies
Yaroslav Pavlyukh, Fabien Bruneval, Arno Förster
Hedin's formalism of functional derivatives is the best-known method for systematically constructing correlated electronic theories, largely due to the success of its lowest-order…
reduced density matrix from iterated linearized Dyson equation
Fabien Bruneval, Erik Verzijl, Arno Förster +1
Iterating the Dyson equation with the static part of the self-energy leads to a concise and possibly improved expression of the one-body reduced density matrix from any self-energy…
Static Effective Hamiltonians for Molecular Systems through RPA-based Downfolding
Erik Verzijl, Arno Förster, Arno Förster
Green's function-based downfolding methods construct effective Hamiltonians of reduced dimension that capture dynamical correlations of an electronic environment through effective…
Transfer learning of GW-Bethe-Salpeter Equation excitation energies
Dario Baum, Arno Förster, Lucas Visscher
A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcit…
qs quasiparticle and -BSE excitation energies of 133,885 molecules
Dario Baum, Arno Förster, Lucas Visscher
Machine learning applications in the chemical sciences, especially when based on neural networks, critically depend on the availability of large quantities of high quality data. As…