Cover Image

Attention-Based Functional-Group Coarse-Graining: A Deep Learning Framework for Molecular Prediction and Design

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Han, Ming, Sun, Ge, de Pablo, Juan J.
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data
by: Stupp, Maximilian, et al.
Published: (2025)

Operator Forces For Coarse-Grained Molecular Dynamics
by: Klein, Leon, et al.
Published: (2025)

Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
by: Murdeshwar, Sanya, et al.
Published: (2026)

Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
by: Duschatko, Blake R., et al.
Published: (2024)

Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
by: Na, Gyoung S., et al.
Published: (2026)

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

Effect of Dispersity on Dynamic Properties of Polymer Melts: Insights from Coarse-Grained Molecular Dynamics Simulations
by: Tejuosho, Taofeek, et al.
Published: (2024)

Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining
by: Shen, Jing, et al.
Published: (2026)

Reaction Dynamics for the [NNO] System from State-Resolved and Coarse-Grained Models
by: Veliz, Juan Carlos San Vicente, et al.
Published: (2025)

Coarse Graining Photo-Isomerization Reactions: Thermodynamic Consistency and Implications for Molecular Ratchets
by: Avanzini, Francesco, et al.
Published: (2025)

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
by: Görlich, Franz, et al.
Published: (2025)

Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach
by: Ray, Pranoy, et al.
Published: (2025)

Adversarial Training for Dynamics Matching in Coarse-Grained Models
by: Wang, Yihang, et al.
Published: (2025)

Coarse-Graining in Quantum Mechanics: Distinguishable and Indistinguishable Particles
by: Sahrmann, Patrick G., et al.
Published: (2025)

Navigating Chemical Space: Multi-Level Bayesian Optimization with Hierarchical Coarse-Graining
by: Walter, Luis J., et al.
Published: (2025)

Hierarchical Framework for Predicting Entropies in Bottom-Up Coarse-Grained Models
by: Jin, Jaehyeok, et al.
Published: (2023)

Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
by: Sahrmann, Patrick G., et al.
Published: (2026)

Coarse-Grained Geometric Quantum Dynamics in the Tensor Network Representation
by: Sha, Mo, et al.
Published: (2026)

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
by: Chen, Weilong, et al.
Published: (2025)

Martini Mapper: An Automated Fragment-Based Framework for Developing Coarse-Grained Models within the Martini 3 Framework
by: Bigting, Kevin V., et al.
Published: (2025)

High-Dimensional Operator Learning for Molecular Density Functional Theory
by: Yang, Jinni, et al.
Published: (2024)

Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
by: Olowookere, Feranmi V., et al.
Published: (2025)

Thermodynamic Descriptors from Molecular Dynamics as Machine Learning Features for Extrapolable Property Prediction
by: Espejo, Nuria H., et al.
Published: (2026)

Coarse-Grained Boltzmann Generators
by: Chen, Weilong, et al.
Published: (2026)

A Bottom-Up Field-Theoretic Framework via Hierarchical Coarse-Graining: Generalized Mode Theory
by: Jin, Jaehyeok, et al.
Published: (2025)

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
by: Wang, JingChun, et al.
Published: (2025)

Reactive Coarse Grained Force Field for Metal-Organic Frameworks applied to Modeling ZIF-8 Self-Assembly
by: Mondal, Sangita, et al.
Published: (2026)

MolCluster: Integrating Graph Neural Network with Community Detection for Coarse-Grained Mapping
by: Zhong, Zhixuan, et al.
Published: (2025)

Generative Deep Learning Framework for Inverse Design of Fuels
by: Yalamanchi, Kiran K., et al.
Published: (2025)

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

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

NepoIP/MM: Towards Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects
by: Song, Ge, et al.
Published: (2025)

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

Understanding Dynamics in Coarse-Grained Models: IV. Connection of Fine-Grained and Coarse-Grained Dynamics with the Stokes-Einstein and Stokes-Einstein-Debye Relations
by: Jin, Jaehyeok, et al.
Published: (2024)

Understanding Dynamics in Coarse-Grained Models: V. Extension of Coarse-Grained Dynamics Theory to Non-Hard Sphere Systems
by: Jin, Jaehyeok, et al.
Published: (2024)

Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
by: Li, Xinyang, et al.
Published: (2023)

Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
by: Zhang, He, et al.
Published: (2023)

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

A Primary Unified Geometric Framework of Molecular Reaction Dynamics Based on the Variational Principle
by: Zhang, Xingyu, et al.
Published: (2026)

The Molecular Quantum electro-Dynamics Research Group in Budapest
by: Matyus, Edit
Published: (2025)