Cover Image

A Systematic Evaluation of Molecular Mixture Behavior Prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Leenhouts, Roel J., Morgan, Nathan K., Green, William, Rittig, Jan G., Vermeire, Florence H.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Pooling Solvent Mixtures for Solvation Free Energy Predictions
by: Leenhouts, Roel J., et al.
Published: (2024)

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

Federated Learning from Molecules to Processes: A Perspective
by: Rittig, Jan G., et al.
Published: (2025)

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

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

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

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

Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)

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

Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024)

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
by: Pavšek, Jan, et al.
Published: (2025)

GraphXForm: Graph transformer for computer-aided molecular design
by: Pirnay, Jonathan, et al.
Published: (2024)

Deep Learning Foundation Models from Classical Molecular Descriptors
by: Burns, Jackson W., et al.
Published: (2025)

Generalizable, Fast, and Accurate DeepQSPR with fastprop
by: Burns, Jackson, et al.
Published: (2024)

Meta-Learning Linear Models for Molecular Property Prediction
by: Pimonova, Yulia, et al.
Published: (2025)

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
by: Gharakhanyan, Vahe, et al.
Published: (2025)

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

Transfer Learning for Molecular Property Predictions from Small Data Sets
by: Kirschbaum, Thorren, et al.
Published: (2024)

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
by: Ji, Shengjiao, et al.
Published: (2025)

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
by: Batatia, Ilyes, et al.
Published: (2026)

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

Grappa -- A Machine Learned Molecular Mechanics Force Field
by: Seute, Leif, et al.
Published: (2024)

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
by: Jiang, Shengli, et al.
Published: (2023)

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

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

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability
by: Panapitiya, Gihan, et al.
Published: (2024)

Image Super-resolution Inspired Electron Density Prediction
by: Li, Chenghan, et al.
Published: (2024)

Active Learning Enables Extrapolation in Molecular Generative Models
by: Antoniuk, Evan R., et al.
Published: (2025)

EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction
by: Tian, Qingwen, et al.
Published: (2024)

E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction
by: Mitnikov, Ilan, et al.
Published: (2024)

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

Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction
by: Ghunaim, Yasir, et al.
Published: (2024)

Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
by: Ni, Yuyan, et al.
Published: (2024)

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

Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
by: Ge, Fuchun, et al.
Published: (2025)

Zero Shot Molecular Generation via Similarity Kernels
by: Elijošius, Rokas, et al.
Published: (2024)

Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
by: Woo, Jeheon, et al.
Published: (2024)

Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst Discovery
by: Geitner, Patrick
Published: (2024)

Geometric Deep Learning for Molecular Crystal Structure Prediction
by: Kilgour, Michael, et al.
Published: (2023)

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
by: Cheng, Lixue, et al.
Published: (2022)