Articles Citing

Machine learning universal bosonic functionals

Jonathan Schmidt ¹, Matteo Fadel ², and Carlos L. Benavides-Riveros ^3,4,*

¹Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, 06120 Halle (Saale), Germany
²Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
³Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Straße 38, 01187 Dresden, Germany
⁴NR-ISM, Division of Ultrafast Processes in Materials (FLASHit), Area della Ricerca di Roma 1, Via Salaria Km 29.3, I-00016 Monterotondo Scalo, Italy

^*carlosbe@pks.mpg.de

Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning

Luis H. Delgado-Granados and David A. Mazziotti

The Journal of Physical Chemistry Letters 17 7206 (2026)

Geometric approach to strongly correlated bosons: From N -representability to the generalized Bose-Einstein condensate force

Chih-Chun Wang and Christian Schilling

Phys. Rev. B 113 224511 (2026)

Advancing Natural Orbital Functional Calculations through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems

Juan Felipe Huan Lew-Yee, Jorge M. del Campo, and Mario Piris

Phys. Rev. Lett. 134 206401 (2025)

Machine Learning of Two-Electron Reduced Density Matrices for Many-Body Problems

Luis H. Delgado-Granados, LeeAnn M. Sager-Smith, Kristina Trifonova, and David A. Mazziotti

J. Phys. Chem. Lett. 16 2231 (2025)

Time evolution of natural orbitals in ab initio molecular dynamics

Alejandro Rivero Santamaría and Mario Piris

The Journal of Chemical Physics 160 071102 (2024)

Orbital-free quasidensity functional theory

Carlos L. Benavides-Riveros

Phys. Rev. Research 6 013060 (2024)

Extracting many-body quantum resources within one-body reduced density matrix functional theory

Carlos L. Benavides-Riveros, Tomasz Wasak, and Alessio Recati

Phys. Rev. Research 6 L012052 (2024)

Electron–vibrational renormalization in fullerenes through ab initio and machine learning methods

Pablo García-Risueño, Eva Armengol, Àngel García-Cerdaña, Juan María García-Lastra, and David Carrasco-Busturia

Phys. Chem. Chem. Phys. 26 20310 (2024)

Exploring the potential of natural orbital functionals

Mario Piris

Chem. Sci. 15 17284 (2024)

No title available

Mario Piris

No title available

Evi Sijben, Jeroen Jansen, Peter Bosman, and Tanja Alderliesten

Deriving density-matrix functionals for excited states

Julia Liebert and Christian Schilling

SciPost Phys. 14 120 (2023)

An exact one-particle theory of bosonic excitations: from a generalized Hohenberg–Kohn theorem to convexified N-representability

Julia Liebert and Christian Schilling

New J. Phys. 25 013009 (2023)

Quantum simulation of bosons with the contracted quantum eigensolver

Yuchen Wang, LeeAnn M Sager-Smith, and David A Mazziotti

New J. Phys. 25 103005 (2023)

Machine learning electronic structure methods based on the one-electron reduced density matrix

Xuecheng Shao, Lukas Paetow, Mark E. Tuckerman, and Michele Pavanello

Nat Commun 14 6281 (2023)

Elucidating the molecular orbital dependence of the total electronic energy in multireference problems

Jan-Niklas Boyn and David A. Mazziotti

The Journal of Chemical Physics 156 194104 (2022)

Density-matrix functional theory of the attractive Hubbard model: Statistical analogy of pairing correlations

T. S. Müller and G. M. Pastor

Phys. Rev. B 106 245148 (2022)

Machine learning the derivative discontinuity of density-functional theory

Johannes Gedeon, Jonathan Schmidt, Matthew J P Hodgson, Jack Wetherell, Carlos L Benavides-Riveros, and Miguel A L Marques

Mach. Learn.: Sci. Technol. 3 015011 (2022)

Repulsively diverging gradient of the density functional in the reduced density matrix functional theory

Tomasz Maciążek

New J. Phys. 23 113006 (2021)

Select page:

Physical Review Research

Filter

Articles Citing

Machine learning universal bosonic functionals

Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning

Geometric approach to strongly correlated bosons: From N -representability to the generalized Bose-Einstein condensate force

Advancing Natural Orbital Functional Calculations through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems

Machine Learning of Two-Electron Reduced Density Matrices for Many-Body Problems

Time evolution of natural orbitals in ab initio molecular dynamics

Orbital-free quasidensity functional theory

Extracting many-body quantum resources within one-body reduced density matrix functional theory

Electron–vibrational renormalization in fullerenes through ab initio and machine learning methods

Exploring the potential of natural orbital functionals

No title available

No title available

Deriving density-matrix functionals for excited states

An exact one-particle theory of bosonic excitations: from a generalized Hohenberg–Kohn theorem to convexified N-representability

Quantum simulation of bosons with the contracted quantum eigensolver

Machine learning electronic structure methods based on the one-electron reduced density matrix

Elucidating the molecular orbital dependence of the total electronic energy in multireference problems

Density-matrix functional theory of the attractive Hubbard model: Statistical analogy of pairing correlations

Machine learning the derivative discontinuity of density-functional theory

Repulsively diverging gradient of the density functional in the reduced density matrix functional theory

Filter

Filter

Article Lookup

Enter a citation