5 papers
Transferable Machine Learning of Electronic Hamiltonians with Superposition-of-Atomic-Potentials Features
Chaoqun Zhang, Christian Venturella, Enzhi Chen +1
Machine learning (ML) of electronic Hamiltonians offers a unified route to electronic wave functions and physical observables. We introduce a Hamiltonian learning framework built o…
Resolving Finite-Size Errors in EOM-CCSD Band Gaps of Solids with Interacting-Bath Dynamical Embedding Theory
Jiachen Li, Christopher Hillenbrand, Christian Venturella +2
Periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD) has shown promise for quantitative calculations of band structures in solids. Howev…
Low-Scaling Many-Body Green's Function Calculations for Molecular Systems via Interacting-Bath Dynamical Embedding Theory
Christian Venturella, Jiachen Li, Tianyu Zhu
We present a molecular extension of our recently proposed Green's function embedding method, interacting-bath dynamical embedding theory (ibDET), for computing charged excitation e…
Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions
Christian Venturella, Jiachen Li, Christopher Hillenbrand +3
Quantum many-body methods provide a systematic route to computing electronic properties of molecules and materials, but high computational costs restrict their use in large-scale a…
Machine Learning Many-Body Green's Functions for Molecular Excitation Spectra
Christian Venturella, Christopher Hillenbrand, Jiachen Li +1
We present a machine learning (ML) framework for predicting Green's functions of molecular systems, from which photoemission spectra and quasiparticle energies at quantum many-body…