Image de couverture de livre

Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Huang, Kevin Han, Zhan, Ni, Ertekin, Elif, Orbanz, Peter, Adams, Ryan P.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Materials Science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Single Architecture for Representing Invariance Under Any Space Group
par: Zhang, Cindy Y., et autres
Publié: (2025)

Space Group Equivariant Crystal Diffusion
par: Chang, Rees, et autres
Publié: (2025)

SLayerGen: a Crystal Generative Model for all Space and Layer Groups
par: Chang, Rees, et autres
Publié: (2026)

Molecule Graph Networks with Many-body Equivariant Interactions
par: Mao, Zetian, et autres
Publié: (2024)

Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
par: Gao, Nicholas, et autres
Publié: (2024)

Lagrangian Neural Networks for Reversible Dissipative Evolution
par: Sundararaghavan, Veera, et autres
Publié: (2024)

Gaussian and Non-Gaussian Universality of Data Augmentation
par: Huang, Kevin Han, et autres
Publié: (2022)

Do Graph Neural Networks Work for High Entropy Alloys?
par: Zhang, Hengrui, et autres
Publié: (2024)

A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
par: Wen, Mingjie, et autres
Publié: (2025)

ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
par: Huang, Jiao, et autres
Publié: (2024)

Efficiently Vectorized MCMC on Modern Accelerators
par: Dance, Hugh, et autres
Publié: (2025)

Generating Symmetric Materials using Latent Flow Matching
par: Karmush, Anmar, et autres
Publié: (2026)

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials
par: Koker, Teddy, et autres
Publié: (2023)

Global optimality under amenable symmetry constraints
par: Orbanz, Peter
Publié: (2024)

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
par: Lee, Wei Shan, et autres
Publié: (2025)

Orb: A Fast, Scalable Neural Network Potential
par: Neumann, Mark, et autres
Publié: (2024)

CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
par: Liang, Chen, et autres
Publié: (2025)

3D Mapping of Defects and Moiré Corrugations via Electron Ptychography Atomic Coordinate Retrieval
par: Huang, Jeffrey, et autres
Publié: (2025)

Kolmogorov-Arnold Neural Networks for High-Entropy Alloys Design
par: Bandyopadhyay, Yagnik, et autres
Publié: (2024)

Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors
par: Gu, Qiangqiang, et autres
Publié: (2025)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
par: Li, Chaojian, et autres
Publié: (2025)

Automated Construction of Artificial Lattice Structures with Designer Electronic States
par: Narasimha, Ganesh, et autres
Publié: (2025)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
par: Jia, Shuyi, et autres
Publié: (2025)

Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations
par: Proppe, Andrew H., et autres
Publié: (2024)

Investigation of Ga interstitial and vacancy diffusion in $β$-Ga$_2$O$_3$ via split defects: a direct approach via master diffusion equations
par: Lee, Channyung, et autres
Publié: (2024)

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
par: Petković, Marko, et autres
Publié: (2024)

Stress Predictions in Polycrystal Plasticity using Graph Neural Networks with Subgraph Training
par: Zhai, Hanfeng
Publié: (2024)

Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
par: Song, Siyuan, et autres
Publié: (2023)

Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
par: Robertson, Andreas E., et autres
Publié: (2025)

Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution
par: Kakhandiki, Pranav, et autres
Publié: (2025)

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling
par: Taj, Reyhaneh
Publié: (2024)

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks
par: Tian, Zhihui, et autres
Publié: (2025)

Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances
par: Musielewicz, Joseph, et autres
Publié: (2024)

Graph Neural Network Prediction of Nonlinear Optical Properties
par: Alkabakibi, Yomn, et autres
Publié: (2025)

Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites
par: Petković, Marko, et autres
Publié: (2025)

Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
par: Sun, Yixuan, et autres
Publié: (2020)

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
par: Schoener, Brandon, et autres
Publié: (2026)

SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning
par: Ueno, Koki, et autres
Publié: (2024)

On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach
par: Rensmeyer, Tim, et autres
Publié: (2025)

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
par: Polat, Can, et autres
Publié: (2025)