Imagen de Portada

Open-Source Fermionic Neural Networks with Ionic Charge Initialization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Pranesh, Shai, Zhu, Shang, Viswanathan, Venkat, Ramsundar, Bharath
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Chemical Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
por: Rittig, Jan G., et al.
Publicado: (2022)

DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library
por: Siguenza, Jose, et al.
Publicado: (2025)

Open-Source Protein Language Models for Function Prediction and Protein Design
por: Pandi, Shivasankaran Vanaja, et al.
Publicado: (2024)

Open Source Infrastructure for Automatic Cell Segmentation
por: Menezes, Aaron Rock, et al.
Publicado: (2024)

Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model
por: Zhu, Jianshen, et al.
Publicado: (2025)

Graph Neural Networks for Surfactant Multi-Property Prediction
por: Brozos, Christoforos, et al.
Publicado: (2024)

Implicit Delta Learning of High Fidelity Neural Network Potentials
por: Thaler, Stephan, et al.
Publicado: (2024)

Highly Accurate Real-space Electron Densities with Neural Networks
por: Cheng, Lixue, et al.
Publicado: (2024)

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
por: Pavšek, Jan, et al.
Publicado: (2026)

OpenQDC: Open Quantum Data Commons
por: Gabellini, Cristian, et al.
Publicado: (2024)

StringNET: Neural Network based Variational Method for Transition Pathways
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)

Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
por: Brozos, Christoforos, et al.
Publicado: (2024)

ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals
por: Elsborg, Jonas, et al.
Publicado: (2025)

Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
por: Shinkle, Emily, et al.
Publicado: (2024)

Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction
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)

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
por: Xia, Junfan, et al.
Publicado: (2025)

Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
por: Marks, Jonah, et al.
Publicado: (2025)

ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures
por: Fan, Jinming, et al.
Publicado: (2026)

Tokenization for Molecular Foundation Models
por: Wadell, Alexius, et al.
Publicado: (2024)

FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
por: Plé, Thomas, et al.
Publicado: (2024)

Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
por: Koenig, Benjamin C., et al.
Publicado: (2025)

Toward Routine CSP of Pharmaceuticals: A Fully Automated Protocol Using Neural Network Potentials
por: Glick, Zachary L., et al.
Publicado: (2025)

MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
por: Yang, Lake, et al.
Publicado: (2026)

EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
por: Wang, Yuanqing, et al.
Publicado: (2023)

Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer
por: Pengmei, Zihan, et al.
Publicado: (2024)

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
por: Gouraud, Nicolaï, et al.
Publicado: (2026)

Generative Model for Constructing Reaction Path from Initial to Final States
por: Hayashi, Akihide, et al.
Publicado: (2024)

GemNet: Universal Directional Graph Neural Networks for Molecules
por: Gasteiger, Johannes, et al.
Publicado: (2021)

$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
por: Khrabrov, Kuzma, et al.
Publicado: (2024)

Foundation Models for Discovery and Exploration in Chemical Space
por: Wadell, Alexius, et al.
Publicado: (2025)

Self-supervised Pretraining for Partial Differential Equations
por: Madhavan, Varun, et al.
Publicado: (2024)

CompleteRXN: Toward Completing Open Chemical Reaction Databases
por: Vogel, Gabriel, et al.
Publicado: (2026)

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
por: Shteingolts, S. A., et al.
Publicado: (2026)

Equivariant Matrix Function Neural Networks
por: Batatia, Ilyes, et al.
Publicado: (2023)

Neural Network Emulator for Atmospheric Chemical ODE
por: Liu, Zhi-Song, et al.
Publicado: (2024)

Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes
por: Simeon, Guillem, et al.
Publicado: (2024)

Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)
por: Koenig, Benjamin C., et al.
Publicado: (2025)

Pretraining Strategy for Neural Potentials
por: Zhang, Zehua, et al.
Publicado: (2024)