Imagen de Portada

ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Fan, Jinming, Qian, Chao, Huck, Wilhelm T. S., Robinson, William E., Zhou, Shaodong
Formato:	Preprint
Publicado:	2026
Materias:	Chemical Physics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems
por: Fan, Jinming, et al.
Publicado: (2025)

DeepHartree: A Poisson-Coupled Neural Field for Scalable Density Functional Theory
por: Wu, Jiankun, et al.
Publicado: (2026)

Implicit Neural Representations for Chemical Reaction Paths
por: Ramakrishnan, Kalyan, et al.
Publicado: (2025)

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

Navigating Chemical Space with Latent Flows
por: Wei, Guanghao, et al.
Publicado: (2024)

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
por: Thöni, Anna C. M., et al.
Publicado: (2025)

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

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)

A Systematic Evaluation of Molecular Mixture Behavior Prediction
por: Leenhouts, Roel J., et al.
Publicado: (2026)

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

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

StringNET: Neural Network based Variational Method for Transition Pathways
por: Han, Jiayue, et al.
Publicado: (2024)

Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations
por: Schopmans, Henrik, et al.
Publicado: (2024)

Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study
por: Gupta, Aryan
Publicado: (2025)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
por: Liu, Yuzhi, et al.
Publicado: (2026)

Machine Learning Multiscale Interactions
por: Solé, Àlex, et al.
Publicado: (2026)

Molecular Machine Learning in Chemical Process Design
por: Rittig, Jan G., et al.
Publicado: (2025)

MUBen: Benchmarking the Uncertainty of Molecular Representation Models
por: Li, Yinghao, et al.
Publicado: (2023)

ChemKANs for Combustion Chemistry Modeling and Acceleration
por: Koenig, Benjamin C., et al.
Publicado: (2025)

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems
por: Kang, Beom Seok, et al.
Publicado: (2025)

Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
por: Ali, Sarwan, et al.
Publicado: (2024)

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

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
por: Sanocki, Michal, et al.
Publicado: (2025)

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
por: Varga-Umbrich, Eszter, et al.
Publicado: (2026)

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
por: Ye, Xinwu, et al.
Publicado: (2026)

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

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

Open-Source Fermionic Neural Networks with Ionic Charge Initialization
por: Pranesh, Shai, et al.
Publicado: (2024)

Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties
por: Vinod, Vivin, et al.
Publicado: (2024)

Chemically-Informed Machine Learning Approach for Prediction of Reactivity Ratios in Radical Copolymerization
por: Safari, Habibollah, et al.
Publicado: (2025)

An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning
por: Jiao, Rui, et al.
Publicado: (2024)

Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
por: Boiko, Daniil A., et al.
Publicado: (2024)

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
por: Brozos, Christoforos, et al.
Publicado: (2024)

NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models
por: Qiao, Zhuoran, et al.
Publicado: (2024)

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)

Bayesian Flow Is All You Need to Sample Out-of-Distribution Chemical Spaces
por: Tao, Nianze, et al.
Publicado: (2024)

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
por: Peng, Jiayu, et al.
Publicado: (2024)

Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data
por: Zhang, Chengwei, et al.
Publicado: (2023)

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