Philipp Hansmann

1 to 6 of 31

07NOV 2025
preprint
Natural-Orbital-Based Neural Network Configuration Interaction
We are very happy to announce another arXiv preprint together with our Icelandic colleagues: This work introduces a simple yet powerful extension to Neural-Network Configuration Interaction (NNCI) by incorporating natural orbitals (NOs)—the eigenfunctions of the one-particle density matrix—as an optimized single-particle basis. The study demonstrates that even approximate NOs, derived from intermediate many-body solutions, dramatically […]We are very happy to announce another arXiv preprint together with our Icelandic colleagues: This work introduces a simple yet powerful extension to Neural-Network Configuration Interaction (NNCI) by incorporating natural orbitals (NOs)—the eigenfunctions of the one-particle density matrix—as an optimized single-particle basis. The study demonstrates that even approximate NOs, derived from intermediate many-body solutions, dramatically […]
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30OCT 2025
preprint
Orbital Optimization and NNCI Calculations of Rydberg States
We are excited to announce the latest results from the collaboration with our Icelandic friends from theoretical chemistry as new preprint on arXiv. Our joint work tackles the long-standing challenge of accurately describing Rydberg excited states—molecular states characterized by extremely diffuse electron distributions that are poorly captured by standard atomic basis sets. The study introduces […]We are excited to announce the latest results from the collaboration with our Icelandic friends from theoretical chemistry as new preprint on arXiv. Our joint work tackles the long-standing challenge of accurately describing Rydberg excited states—molecular states characterized by extremely diffuse electron distributions that are poorly captured by standard atomic basis sets. The study introduces […]
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02AUG 2025
preprint
Real-space quantum embedding for disorder and correlations with C-CDMFT
As collaborators of the group of Prof. Dr. Thomas Schaefer (Univerity of Trieste) we’re glad to announce our latest joint preprint introducing C-CDMFT, a real-space cluster method that treats electronic interactions and disorder on equal footing. The approach combines the molecular coherent potential approximation (MCPA)—including its Blackman-Esterling-Berk (BEB) treatment of off-diagonal disorder—with cellular DMFT (CDMFT) […]As collaborators of the group of Prof. Dr. Thomas Schaefer (Univerity of Trieste) we’re glad to announce our latest joint preprint introducing C-CDMFT, a real-space cluster method that treats electronic interactions and disorder on equal footing. The approach combines the molecular coherent potential approximation (MCPA)—including its Blackman-Esterling-Berk (BEB) treatment of off-diagonal disorder—with cellular DMFT (CDMFT) […]
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28MAY 2025
publication
Single-particle spectra and magnetic susceptibility in the Emery model: A dynamical mean-field perspective
We are pleased to announce the publication of our new paper in SciPost Physics. In our article, we investigate dynamical mean-field calculations of the three-band Emery model, focusing on the one- and two-particle levels for material-realistic parameters of high-(Tc) superconductors. Our results show that even within DMFT, the intrinsic multi-orbital nature of the model introduces […]
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28FEB 2025
publication
Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure
We are pleased to announce our new joint publication with Theoretical Chemsits at the University of Iceland in the Journal of Chemical Theory and Computation (JCTC). In this work, we combine Hartree–Fock with a neural-network-supported quantum-cluster solver to introduce the Neural-Network Configuration Interaction (NNCI) method for accurate and efficient electronic-structure calculations. NNCI selectively identifies relevant […]We are pleased to announce our new joint publication with Theoretical Chemsits at the University of Iceland in the Journal of Chemical Theory and Computation (JCTC). In this work, we combine Hartree–Fock with a neural-network-supported quantum-cluster solver to introduce the Neural-Network Configuration Interaction (NNCI) method for accurate and efficient electronic-structure calculations. NNCI selectively identifies relevant […]
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20JAN 2025
publication
Neural-network-supported basis optimizer for configuration interaction problems
We are pleased to announce the publication of our new paper in Physical Review B. Our work introduces a neural-network approach to optimize the selection of Slater determinants in configuration interaction (CI) calculations for strongly correlated quantum systems. Using a neural classifier and active learning, the method efficiently identifies the most relevant determinants for the ground-state […]We are pleased to announce the publication of our new paper in Physical Review B. Our work introduces a neural-network approach to optimize the selection of Slater determinants in configuration interaction (CI) calculations for strongly correlated quantum systems. Using a neural classifier and active learning, the method efficiently identifies the most relevant determinants for the ground-state […]
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