Saved in:
| Main Authors: | Zhong, Zhixuan, Ma, Linbo, Jiang, Jian |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.20893 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation
by: Zhong, Zhixuan, et al.
Published: (2024)
by: Zhong, Zhixuan, et al.
Published: (2024)
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
by: Shinkle, Emily, et al.
Published: (2024)
by: Shinkle, Emily, et al.
Published: (2024)
Operator Forces For Coarse-Grained Molecular Dynamics
by: Klein, Leon, et al.
Published: (2025)
by: Klein, Leon, et al.
Published: (2025)
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
by: Duschatko, Blake R., et al.
Published: (2024)
by: Duschatko, Blake R., et al.
Published: (2024)
Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
by: Görlich, Franz, et al.
Published: (2025)
by: Görlich, Franz, et al.
Published: (2025)
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
by: Chen, Weilong, et al.
Published: (2025)
by: Chen, Weilong, et al.
Published: (2025)
Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
by: Murdeshwar, Sanya, et al.
Published: (2026)
by: Murdeshwar, Sanya, et al.
Published: (2026)
Coarse-Grained Geometric Quantum Dynamics in the Tensor Network Representation
by: Sha, Mo, et al.
Published: (2026)
by: Sha, Mo, et al.
Published: (2026)
Scaling Graph Neural Networks to Large Proteins
by: Airas, Justin, et al.
Published: (2024)
by: Airas, Justin, 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)
by: Jin, Jaehyeok, 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)
by: Jin, Jaehyeok, et al.
Published: (2024)
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data
by: Stupp, Maximilian, et al.
Published: (2025)
by: Stupp, Maximilian, et al.
Published: (2025)
Experimentally Accurate Graph Neural Network Predictions of Core-Electron Binding Energies
by: Fouda, Adam E. A., et al.
Published: (2026)
by: Fouda, Adam E. A., et al.
Published: (2026)
SchrödingerNet: A Universal Neural Network Solver for The Schrödinger Equation
by: Zhang, Yaolong, et al.
Published: (2024)
by: Zhang, Yaolong, et al.
Published: (2024)
GemNet: Universal Directional Graph Neural Networks for Molecules
by: Gasteiger, Johannes, et al.
Published: (2021)
by: Gasteiger, Johannes, et al.
Published: (2021)
Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
by: Na, Gyoung S., et al.
Published: (2026)
by: Na, Gyoung S., et al.
Published: (2026)
Adversarial Training for Dynamics Matching in Coarse-Grained Models
by: Wang, Yihang, et al.
Published: (2025)
by: Wang, Yihang, et al.
Published: (2025)
Coarse-Graining in Quantum Mechanics: Distinguishable and Indistinguishable Particles
by: Sahrmann, Patrick G., et al.
Published: (2025)
by: Sahrmann, Patrick G., 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)
by: Bigting, Kevin V., et al.
Published: (2025)
MolDStruct: modelling the dynamics and structure of matter exposed to ultrafast X-ray lasers with hybrid collisional-radiative/molecular dynamics
by: Dawod, Ibrahim, et al.
Published: (2024)
by: Dawod, Ibrahim, et al.
Published: (2024)
Hierarchical Framework for Predicting Entropies in Bottom-Up Coarse-Grained Models
by: Jin, Jaehyeok, et al.
Published: (2023)
by: Jin, Jaehyeok, et al.
Published: (2023)
Microscopic Theory of Density Scaling: Coarse-Graining in Space and Time
by: Jin, Jaehyeok, et al.
Published: (2024)
by: Jin, Jaehyeok, et al.
Published: (2024)
Rigorous Quantum Thermodynamics from Entropic Path Integral Coarse-Graining
by: Shen, Jing, et al.
Published: (2026)
by: Shen, Jing, et al.
Published: (2026)
Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
by: Chen, Siqi, et al.
Published: (2024)
by: Chen, Siqi, et al.
Published: (2024)
Navigating Chemical Space: Multi-Level Bayesian Optimization with Hierarchical Coarse-Graining
by: Walter, Luis J., et al.
Published: (2025)
by: Walter, Luis J., et al.
Published: (2025)
Reaction Dynamics for the [NNO] System from State-Resolved and Coarse-Grained Models
by: Veliz, Juan Carlos San Vicente, et al.
Published: (2025)
by: Veliz, Juan Carlos San Vicente, et al.
Published: (2025)
Knowledge Distillation of Noisy Force Labels for Improved Coarse-Grained Force Fields
by: Olowookere, Feranmi V., et al.
Published: (2025)
by: Olowookere, Feranmi V., et al.
Published: (2025)
Coarse-graining bistability with the Martini force field
by: Muratov, Alexander D., et al.
Published: (2024)
by: Muratov, Alexander D., et al.
Published: (2024)
Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study
by: Gupta, Aryan
Published: (2025)
by: Gupta, Aryan
Published: (2025)
Coarse-Grained Boltzmann Generators
by: Chen, Weilong, et al.
Published: (2026)
by: Chen, Weilong, et al.
Published: (2026)
iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes
by: Chiavazzo, Eliodoro, et al.
Published: (2016)
by: Chiavazzo, Eliodoro, et al.
Published: (2016)
A Bottom-Up Field-Theoretic Framework via Hierarchical Coarse-Graining: Generalized Mode Theory
by: Jin, Jaehyeok, et al.
Published: (2025)
by: Jin, Jaehyeok, et al.
Published: (2025)
Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory
by: Pan, Runtong, et al.
Published: (2025)
by: Pan, Runtong, et al.
Published: (2025)
RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models
by: Liu, Shikun, et al.
Published: (2025)
by: Liu, Shikun, et al.
Published: (2025)
Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework
by: Lu, Xinyu, et al.
Published: (2025)
by: Lu, Xinyu, et al.
Published: (2025)
Theory and Modeling of Transport for Simple Fluids in Nanoporous Materials: From Microscopic to Coarse-Grained Descriptions
by: Schlaich, Alexander, et al.
Published: (2024)
by: Schlaich, Alexander, et al.
Published: (2024)
Double Ionization Potential Equation-of-Motion Coupled-Cluster Approach with Full Inclusion of 4-Hole-2-Particle Excitations and Three-Body Clusters
by: Gururangan, Karthik, et al.
Published: (2024)
by: Gururangan, Karthik, et al.
Published: (2024)
Attention-Based Functional-Group Coarse-Graining: A Deep Learning Framework for Molecular Prediction and Design
by: Han, Ming, et al.
Published: (2025)
by: Han, Ming, et al.
Published: (2025)
Effect of Dispersity on Dynamic Properties of Polymer Melts: Insights from Coarse-Grained Molecular Dynamics Simulations
by: Tejuosho, Taofeek, et al.
Published: (2024)
by: Tejuosho, Taofeek, et al.
Published: (2024)
Inverse Design Method with Enhanced Sampling for Complex Open Crystals: Application to Novel Zeolite Self-Assembly in a Coarse-Grained Model
by: Wang, Chaohong, et al.
Published: (2024)
by: Wang, Chaohong, et al.
Published: (2024)
Similar Items
-
A Neural-Network-Based Mapping and Optimization Framework for High-Precision Coarse-Grained Simulation
by: Zhong, Zhixuan, et al.
Published: (2024) -
Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
by: Shinkle, Emily, et al.
Published: (2024) -
Operator Forces For Coarse-Grained Molecular Dynamics
by: Klein, Leon, et al.
Published: (2025) -
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
by: Duschatko, Blake R., et al.
Published: (2024) -
Mapping Still Matters: Coarse-Graining with Machine Learning Potentials
by: Görlich, Franz, et al.
Published: (2025)