Saved in:
| Main Authors: | Chen, Siqi, Wang, Zhiqiang, Shen, Yili, Deng, Xianqi, Cheng, Xi, Ju, Cheng-Wei, Yi, Jun, Ling, Guo, Alhmoud, Dieaa, Guan, Hui, Lin, Zhou |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.09320 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Potential Distribution Theory of Alchemical Transfer
by: Azimi, Solmaz, et al.
Published: (2024)
by: Azimi, Solmaz, et al.
Published: (2024)
Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
by: Pelletier, Vivienne, et al.
Published: (2026)
by: Pelletier, Vivienne, et al.
Published: (2026)
Derivative Discontinuity in Many-Body Perturbation Theory and Chemical Potentials in Random Phase Approximation
by: Li, Jiachen, et al.
Published: (2026)
by: Li, Jiachen, et al.
Published: (2026)
Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)
by: Niblett, Samuel P., et al.
Published: (2024)
A General and Transferable Local Hybrid Functional for Electronic Structure Theory and Many-Fermion Approaches
by: Holzer, Christof, et al.
Published: (2024)
by: Holzer, Christof, et al.
Published: (2024)
Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
by: Neubert, Marlen, et al.
Published: (2025)
by: Neubert, Marlen, et al.
Published: (2025)
"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025)
by: Qu, Chen, et al.
Published: (2025)
Exact Screening-Ranged Expansions for Many-Body Electrostatics
by: Siryk, Sergii V., et al.
Published: (2025)
by: Siryk, Sergii V., et al.
Published: (2025)
Quantum Many-Body Simulations of Catalytic Metal Surfaces
by: Cao, Changsu, et al.
Published: (2025)
by: Cao, Changsu, et al.
Published: (2025)
Fragmentation of Virtual Orbitals for Quantum Computing: Reducing Qubit Requirements through Many-Body Expansion
by: Zahariev, Federico, et al.
Published: (2025)
by: Zahariev, Federico, et al.
Published: (2025)
Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
by: Barrett, Rhyan, et al.
Published: (2025)
by: Barrett, Rhyan, et al.
Published: (2025)
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
by: Goodwin, Zachary A. H., et al.
Published: (2024)
by: Goodwin, Zachary A. H., et al.
Published: (2024)
MBE-CASSCF Approach for the Accurate Treatment of Large Active Spaces
by: Greiner, Jonas, et al.
Published: (2024)
by: Greiner, Jonas, et al.
Published: (2024)
Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
by: Zhu, Jia-Xin, et al.
Published: (2024)
by: Zhu, Jia-Xin, et al.
Published: (2024)
Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations
by: Chaton, Kham Lek, et al.
Published: (2026)
by: Chaton, Kham Lek, et al.
Published: (2026)
Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)
by: Chong, Sanggyu, et al.
Published: (2025)
Advancing Surface Chemistry with Large-Scale Ab-Initio Quantum Many-Body Simulations
by: Huang, Zigeng, et al.
Published: (2024)
by: Huang, Zigeng, et al.
Published: (2024)
A Transferable Machine-Learning Model of the Electron Density
by: Grisafi, Andrea, et al.
Published: (2018)
by: Grisafi, Andrea, et al.
Published: (2018)
XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics
by: Dudakov, Ivan V., et al.
Published: (2025)
by: Dudakov, Ivan V., et al.
Published: (2025)
Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning
by: Lyu, Dongyu, et al.
Published: (2024)
by: Lyu, Dongyu, et al.
Published: (2024)
Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
by: Bianchi, Michele Giovanni, et al.
Published: (2025)
by: Bianchi, Michele Giovanni, et al.
Published: (2025)
Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
by: Meyer, Ralf, et al.
Published: (2024)
by: Meyer, Ralf, et al.
Published: (2024)
Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics
by: Zhang, Jiaji, et al.
Published: (2026)
by: Zhang, Jiaji, et al.
Published: (2026)
Unveiling Intrinsic Many-Body Complexity by Compressing Single-Body Triviality
by: Liao, Ke, et al.
Published: (2024)
by: Liao, Ke, et al.
Published: (2024)
Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing
by: Bian, Xuezhi, et al.
Published: (2026)
by: Bian, Xuezhi, et al.
Published: (2026)
Transferable Water Potentials Using Equivariant Neural Networks
by: Maxson, Tristan, et al.
Published: (2024)
by: Maxson, Tristan, et al.
Published: (2024)
All-atomistic Transferable Neural Potentials for Protein Solvation
by: Dey, Rishabh, et al.
Published: (2026)
by: Dey, Rishabh, et al.
Published: (2026)
Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions
by: Venturella, Christian, et al.
Published: (2024)
by: Venturella, Christian, et al.
Published: (2024)
Electrochemical Electron Transfer: Key Concepts, Theories, and Parameterization via Atomistic Simulations
by: Zhang, Mengke, et al.
Published: (2025)
by: Zhang, Mengke, et al.
Published: (2025)
Marcus Theory and The Condon Approximation Revisited II: The Horror of Triplet Energy Transfer
by: DeRosa, Jennifer R., et al.
Published: (2025)
by: DeRosa, Jennifer R., et al.
Published: (2025)
Extracting Many-Body Quantum Resources within One-Body Reduced Density Matrix Functional Theory
by: Benavides-Riveros, Carlos L., et al.
Published: (2023)
by: Benavides-Riveros, Carlos L., et al.
Published: (2023)
Completely Multipolar Model for Many-Body Water-Ion and Ion-Ion Interactions
by: Heindel, J. P., et al.
Published: (2024)
by: Heindel, J. P., et al.
Published: (2024)
Many-Body Basis Set Amelioration Method for Incremental Full Configuration Interaction
by: Hatch, Jeffrey P, et al.
Published: (2024)
by: Hatch, Jeffrey P, et al.
Published: (2024)
Low-Scaling Many-Body Green's Function Calculations for Molecular Systems via Interacting-Bath Dynamical Embedding Theory
by: Venturella, Christian, et al.
Published: (2026)
by: Venturella, Christian, et al.
Published: (2026)
Multi-GPU MBE(3)-OSV-MP2 for Performant Large-Scale ab initio Calculations
by: Liang, Qiujiang, et al.
Published: (2026)
by: Liang, Qiujiang, et al.
Published: (2026)
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
by: Kovács, Dávid Péter, et al.
Published: (2023)
by: Kovács, Dávid Péter, et al.
Published: (2023)
Bipartite Cholesky Graph Networks for Many-Body Quantum Chemistry
by: Khan, Abdul Samad
Published: (2026)
by: Khan, Abdul Samad
Published: (2026)
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)
by: Sahrmann, Patrick G., et al.
Published: (2026)
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)
by: Vazquez-Salazar, Luis Itza, et al.
Published: (2024)
Similar Items
-
Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
by: Chen, Siqi, et al.
Published: (2024) -
Potential Distribution Theory of Alchemical Transfer
by: Azimi, Solmaz, et al.
Published: (2024) -
Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning
by: Pelletier, Vivienne, et al.
Published: (2026) -
Derivative Discontinuity in Many-Body Perturbation Theory and Chemical Potentials in Random Phase Approximation
by: Li, Jiachen, et al.
Published: (2026) -
Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)