Predicting The One-Particle Density Matrix With Machine Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Hazra, S., Patil, U., Sanvito, S. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A charge-density machine-learning workflow for computing the infrared spectrum of molecules
por: Hazra, Suman, et al.
Publicado: (2025)
por: Hazra, Suman, et al.
Publicado: (2025)
A general formalism for machine-learning models based on multipolar-spherical harmonics
por: Domina, Michelangelo, et al.
Publicado: (2025)
por: Domina, Michelangelo, et al.
Publicado: (2025)
Atom-Density Representations for Machine Learning
por: Willatt, Michael J., et al.
Publicado: (2018)
por: Willatt, Michael J., et al.
Publicado: (2018)
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
por: Xin, Zaizhou, et al.
Publicado: (2025)
por: Xin, Zaizhou, et al.
Publicado: (2025)
Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
por: Golub, Pavlo, et al.
Publicado: (2024)
por: Golub, Pavlo, et al.
Publicado: (2024)
Unitary Dynamics for Open Quantum Systems with Density-Matrix Purification
por: Delgado-Granados, Luis H., et al.
Publicado: (2024)
por: Delgado-Granados, Luis H., et al.
Publicado: (2024)
Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning
por: Colglazier, William, et al.
Publicado: (2025)
por: Colglazier, William, et al.
Publicado: (2025)
Impact of Parametrizations of the One-Body Reduced Density Matrix on the Energy Landscape
por: Cartier, Nicolas, et al.
Publicado: (2025)
por: Cartier, Nicolas, et al.
Publicado: (2025)
Computational Model for Predicting Particle Fracture During Electrode Calendering
por: Xu, Jiahui, et al.
Publicado: (2023)
por: Xu, Jiahui, et al.
Publicado: (2023)
Extracting Many-Body Quantum Resources within One-Body Reduced Density Matrix Functional Theory
por: Benavides-Riveros, Carlos L., et al.
Publicado: (2023)
por: Benavides-Riveros, Carlos L., et al.
Publicado: (2023)
Effects of One-particle Reduced Density Matrix Optimization in Variational Quantum Eigensolvers
por: de Lima, Amanda Marques, et al.
Publicado: (2025)
por: de Lima, Amanda Marques, et al.
Publicado: (2025)
Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids
por: Feng, Wei, et al.
Publicado: (2025)
por: Feng, Wei, et al.
Publicado: (2025)
Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning
por: Delgado-Granados, Luis H., et al.
Publicado: (2026)
por: Delgado-Granados, Luis H., et al.
Publicado: (2026)
Definitive Assessment of the Accuracy, Variationality, and Convergence of Relativistic Coupled Cluster and Density Matrix Renormalization Group in 100-Orbital Space
por: Upadhyay, Shiv, et al.
Publicado: (2026)
por: Upadhyay, Shiv, et al.
Publicado: (2026)
Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning
por: Han, Mingjun, et al.
Publicado: (2024)
por: Han, Mingjun, et al.
Publicado: (2024)
Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics
por: Yu, Junting, et al.
Publicado: (2026)
por: Yu, Junting, et al.
Publicado: (2026)
Long-Range Machine Learning of Electron Density for Twisted Bilayer Moiré Materials
por: Lou, Zekun, et al.
Publicado: (2026)
por: Lou, Zekun, et al.
Publicado: (2026)
Machine Learning Wavefunction
por: Battaglia, Stefano
Publicado: (2022)
por: Battaglia, Stefano
Publicado: (2022)
Stable, Fast, and Accurate Kohn-Sham Inversion in Gaussian Basis for Open Shell Molecular and Condensed Phase Systems via Density Matrix Penalization
por: Chai, Ziwei, et al.
Publicado: (2026)
por: Chai, Ziwei, et al.
Publicado: (2026)
Particle-Particle Random Phase Approximation for Predicting Correlated Excited States of Point Defects
por: Li, Jiachen, et al.
Publicado: (2024)
por: Li, Jiachen, et al.
Publicado: (2024)
Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
por: Yescas-Ramos, Zuriel Y., et al.
Publicado: (2026)
por: Yescas-Ramos, Zuriel Y., et al.
Publicado: (2026)
The Software Landscape for the Density Matrix Renormalization Group
por: Sehlstedt, Per, et al.
Publicado: (2025)
por: Sehlstedt, Per, et al.
Publicado: (2025)
Towards Excitations and Dynamical Quantities in Correlated Lattices with Density Matrix Embedding Theory
por: Li, Shuoxue, et al.
Publicado: (2025)
por: Li, Shuoxue, et al.
Publicado: (2025)
A Machine Learning Model for the Chemistry of a Solvated Electron
por: Gao, Ruiqi, et al.
Publicado: (2025)
por: Gao, Ruiqi, et al.
Publicado: (2025)
Molecular Similarity in Machine Learning of Energies in Chemical Reaction Networks
por: Gugler, Stefan, et al.
Publicado: (2025)
por: Gugler, Stefan, et al.
Publicado: (2025)
Providing Machine Learning Potentials with High Quality Uncertainty Estimates
por: Sumer, Zeynep, et al.
Publicado: (2025)
por: Sumer, Zeynep, et al.
Publicado: (2025)
Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
por: Eckhoff, Marco, et al.
Publicado: (2025)
por: Eckhoff, Marco, et al.
Publicado: (2025)
Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
por: Lal, Ashique, et al.
Publicado: (2026)
por: Lal, Ashique, et al.
Publicado: (2026)
Understanding the Density Maximum of Water with Machine Learned Potentials
por: Song, Yizhi, et al.
Publicado: (2026)
por: Song, Yizhi, et al.
Publicado: (2026)
A Transferable Machine-Learning Model of the Electron Density
por: Grisafi, Andrea, et al.
Publicado: (2018)
por: Grisafi, Andrea, et al.
Publicado: (2018)
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
por: Goodwin, Zachary A. H., et al.
Publicado: (2024)
por: Goodwin, Zachary A. H., et al.
Publicado: (2024)
Reduced Density Matrix Functional Theory And A Reduced Formulation Of Density Functional Theory
por: Fredheim, Håkon R., et al.
Publicado: (2025)
por: Fredheim, Håkon R., et al.
Publicado: (2025)
Generative Latent Space Dynamics of Electron Density
por: Chiang, Yuan, et al.
Publicado: (2025)
por: Chiang, Yuan, et al.
Publicado: (2025)
N-Mode Quantized Anharmonic Vibronic Hamiltonians for Matrix Product State Dynamics
por: Barandun, Valentin, et al.
Publicado: (2025)
por: Barandun, Valentin, et al.
Publicado: (2025)
Analyzing Band Gaps in Ensemble Density Functional Theory using Thermodynamic Limits of Finite One-Dimensional Model Systems
por: Kenning, Gregory G. V., et al.
Publicado: (2026)
por: Kenning, Gregory G. V., et al.
Publicado: (2026)
Revolutionising Antibacterial Warfare: Machine Learning and Molecular Dynamics Unveiling Potential Gram-Negative Bacteria Inhibitors
por: Joshi, Pritish, et al.
Publicado: (2025)
por: Joshi, Pritish, et al.
Publicado: (2025)
HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials
por: Cui, Taoyong, et al.
Publicado: (2025)
por: Cui, Taoyong, et al.
Publicado: (2025)
Witnessing Entanglement in Mixed-Particle Quantum Systems
por: Avdic, Irma, et al.
Publicado: (2025)
por: Avdic, Irma, et al.
Publicado: (2025)
Polaritonic Chemistry using the Density Matrix Renormalization Group Method
por: Matoušek, Mikuláš, et al.
Publicado: (2024)
por: Matoušek, Mikuláš, et al.
Publicado: (2024)
Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables
por: Trizio, Enrico, et al.
Publicado: (2024)
por: Trizio, Enrico, et al.
Publicado: (2024)
Ejemplares similares
-
A charge-density machine-learning workflow for computing the infrared spectrum of molecules
por: Hazra, Suman, et al.
Publicado: (2025) -
A general formalism for machine-learning models based on multipolar-spherical harmonics
por: Domina, Michelangelo, et al.
Publicado: (2025) -
Atom-Density Representations for Machine Learning
por: Willatt, Michael J., et al.
Publicado: (2018) -
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
por: Xin, Zaizhou, et al.
Publicado: (2025) -
Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
por: Golub, Pavlo, et al.
Publicado: (2024)