Gaussian Plane-Wave Neural Operator for Electron Density Estimation
Fuente:
arXiv
Guardado en:
| Autores principales: | Kim, Seongsu, Ahn, Sungsoo |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
por: Elsborg, Jonas, et al.
Publicado: (2026)
por: Elsborg, Jonas, et al.
Publicado: (2026)
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
por: Kim, Taewon, et al.
Publicado: (2024)
por: Kim, Taewon, et al.
Publicado: (2024)
Highly Accurate Real-space Electron Densities with Neural Networks
por: Cheng, Lixue, et al.
Publicado: (2024)
por: Cheng, Lixue, et al.
Publicado: (2024)
Flexible MOF Generation with Torsion-Aware Flow Matching
por: Kim, Nayoung, et al.
Publicado: (2025)
por: Kim, Nayoung, et al.
Publicado: (2025)
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
por: Gao, Nicholas, et al.
Publicado: (2024)
por: Gao, Nicholas, et al.
Publicado: (2024)
Image Super-resolution Inspired Electron Density Prediction
por: Li, Chenghan, et al.
Publicado: (2024)
por: Li, Chenghan, et al.
Publicado: (2024)
Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks
por: Kwak, Bumju, et al.
Publicado: (2024)
por: Kwak, Bumju, et al.
Publicado: (2024)
Learning Equivariant Non-Local Electron Density Functionals
por: Gao, Nicholas, et al.
Publicado: (2024)
por: Gao, Nicholas, et al.
Publicado: (2024)
Fast and Generalizable parameter-embedded Neural Operators for Lithium-Ion Battery Simulation
por: Panahi, Amir Ali, et al.
Publicado: (2025)
por: Panahi, Amir Ali, et al.
Publicado: (2025)
E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction
por: Mitnikov, Ilan, et al.
Publicado: (2024)
por: Mitnikov, Ilan, et al.
Publicado: (2024)
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
por: Scherbela, Michael, et al.
Publicado: (2025)
por: Scherbela, Michael, et al.
Publicado: (2025)
Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
por: Gao, Nicholas, et al.
Publicado: (2026)
por: Gao, Nicholas, et al.
Publicado: (2026)
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
por: Gao, Nicholas, et al.
Publicado: (2024)
por: Gao, Nicholas, et al.
Publicado: (2024)
Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
por: Remme, Roman, et al.
Publicado: (2026)
por: Remme, Roman, et al.
Publicado: (2026)
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
por: Zhang, He, et al.
Publicado: (2023)
por: Zhang, He, et al.
Publicado: (2023)
Pretraining Strategy for Neural Potentials
por: Zhang, Zehua, et al.
Publicado: (2024)
por: Zhang, Zehua, et al.
Publicado: (2024)
Density Estimation via Binless Multidimensional Integration
por: Carli, Matteo, et al.
Publicado: (2024)
por: Carli, Matteo, et al.
Publicado: (2024)
Multi-Objective Bayesian Optimization with Independent Tanimoto Kernel Gaussian Processes for Diverse Pareto Front Exploration
por: Yong, Anabel
Publicado: (2025)
por: Yong, Anabel
Publicado: (2025)
Implicit Neural Representations for Chemical Reaction Paths
por: Ramakrishnan, Kalyan, et al.
Publicado: (2025)
por: Ramakrishnan, Kalyan, et al.
Publicado: (2025)
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
por: Woo, Jeheon, et al.
Publicado: (2024)
por: Woo, Jeheon, et al.
Publicado: (2024)
Operator Forces For Coarse-Grained Molecular Dynamics
por: Klein, Leon, et al.
Publicado: (2025)
por: Klein, Leon, et al.
Publicado: (2025)
Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
por: Cheng, Lixue, et al.
Publicado: (2022)
por: Cheng, Lixue, et al.
Publicado: (2022)
Graph Neural Networks for Surfactant Multi-Property Prediction
por: Brozos, Christoforos, et al.
Publicado: (2024)
por: Brozos, Christoforos, et al.
Publicado: (2024)
All-atomistic Transferable Neural Potentials for Protein Solvation
por: Dey, Rishabh, et al.
Publicado: (2026)
por: Dey, Rishabh, et al.
Publicado: (2026)
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
por: Kim, Nayoung, et al.
Publicado: (2024)
por: Kim, Nayoung, et al.
Publicado: (2024)
Implicit Delta Learning of High Fidelity Neural Network Potentials
por: Thaler, Stephan, et al.
Publicado: (2024)
por: Thaler, Stephan, et al.
Publicado: (2024)
Open-Source Fermionic Neural Networks with Ionic Charge Initialization
por: Pranesh, Shai, et al.
Publicado: (2024)
por: Pranesh, Shai, et al.
Publicado: (2024)
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
por: Pavšek, Jan, et al.
Publicado: (2026)
por: Pavšek, Jan, et al.
Publicado: (2026)
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
por: Zhang, He, et al.
Publicado: (2024)
por: Zhang, He, et al.
Publicado: (2024)
StringNET: Neural Network based Variational Method for Transition Pathways
por: Han, Jiayue, et al.
Publicado: (2024)
por: Han, Jiayue, et al.
Publicado: (2024)
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
por: Brozos, Christoforos, et al.
Publicado: (2024)
por: Brozos, Christoforos, et al.
Publicado: (2024)
Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
por: Brozos, Christoforos, et al.
Publicado: (2024)
por: Brozos, Christoforos, et al.
Publicado: (2024)
High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction
por: Kim, Seongsu, et al.
Publicado: (2025)
por: Kim, Seongsu, et al.
Publicado: (2025)
Self-consistent Validation for Machine Learning Electronic Structure
por: Hu, Gengyuan, et al.
Publicado: (2024)
por: Hu, Gengyuan, et al.
Publicado: (2024)
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
por: Shinkle, Emily, et al.
Publicado: (2024)
por: Shinkle, Emily, et al.
Publicado: (2024)
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
por: Thiede, Luca, et al.
Publicado: (2026)
por: Thiede, Luca, et al.
Publicado: (2026)
Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction
por: Medina, Edgar Ivan Sanchez, et al.
Publicado: (2025)
por: Medina, Edgar Ivan Sanchez, et al.
Publicado: (2025)
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
por: Käser, Silvan, et al.
Publicado: (2024)
por: Käser, Silvan, et al.
Publicado: (2024)
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
por: Rittig, Jan G., et al.
Publicado: (2022)
por: Rittig, Jan G., et al.
Publicado: (2022)
Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
por: Xia, Junfan, et al.
Publicado: (2025)
por: Xia, Junfan, et al.
Publicado: (2025)
Ejemplares similares
-
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
por: Elsborg, Jonas, et al.
Publicado: (2026) -
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
por: Kim, Taewon, et al.
Publicado: (2024) -
Highly Accurate Real-space Electron Densities with Neural Networks
por: Cheng, Lixue, et al.
Publicado: (2024) -
Flexible MOF Generation with Torsion-Aware Flow Matching
por: Kim, Nayoung, et al.
Publicado: (2025) -
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
por: Gao, Nicholas, et al.
Publicado: (2024)