Implicit Delta Learning of High Fidelity Neural Network Potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Thaler, Stephan, Gabellini, Cristian, Shenoy, Nikhil, Tossou, Prudencio |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
OpenQDC: Open Quantum Data Commons
by: Gabellini, Cristian, et al.
Published: (2024)
by: Gabellini, Cristian, et al.
Published: (2024)
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
by: Fuchs, Paul, et al.
Published: (2024)
by: Fuchs, Paul, et al.
Published: (2024)
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
by: Fuchs, Paul, et al.
Published: (2025)
by: Fuchs, Paul, et al.
Published: (2025)
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
by: Kapuśniak, Kacper, et al.
Published: (2025)
by: Kapuśniak, Kacper, et al.
Published: (2025)
Implicit Neural Representations for Chemical Reaction Paths
by: Ramakrishnan, Kalyan, et al.
Published: (2025)
by: Ramakrishnan, Kalyan, et al.
Published: (2025)
GemNet: Universal Directional Graph Neural Networks for Molecules
by: Gasteiger, Johannes, et al.
Published: (2021)
by: Gasteiger, Johannes, et al.
Published: (2021)
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
by: Käser, Silvan, et al.
Published: (2024)
by: Käser, Silvan, et al.
Published: (2024)
Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
by: Xia, Junfan, et al.
Published: (2025)
by: Xia, Junfan, et al.
Published: (2025)
Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
by: Marks, Jonah, et al.
Published: (2025)
by: Marks, Jonah, et al.
Published: (2025)
Pretraining Strategy for Neural Potentials
by: Zhang, Zehua, et al.
Published: (2024)
by: Zhang, Zehua, et al.
Published: (2024)
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
by: Plé, Thomas, et al.
Published: (2024)
by: Plé, Thomas, et al.
Published: (2024)
Toward Routine CSP of Pharmaceuticals: A Fully Automated Protocol Using Neural Network Potentials
by: Glick, Zachary L., et al.
Published: (2025)
by: Glick, Zachary L., et al.
Published: (2025)
Highly Accurate Real-space Electron Densities with Neural Networks
by: Cheng, Lixue, et al.
Published: (2024)
by: Cheng, Lixue, et al.
Published: (2024)
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
by: Gao, Nicholas, et al.
Published: (2024)
by: Gao, Nicholas, et al.
Published: (2024)
Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces
by: Gouraud, Nicolaï, et al.
Published: (2026)
by: Gouraud, Nicolaï, et al.
Published: (2026)
All-atomistic Transferable Neural Potentials for Protein Solvation
by: Dey, Rishabh, et al.
Published: (2026)
by: Dey, Rishabh, et al.
Published: (2026)
Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes
by: Simeon, Guillem, et al.
Published: (2024)
by: Simeon, Guillem, et al.
Published: (2024)
$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
by: Khrabrov, Kuzma, et al.
Published: (2024)
by: Khrabrov, Kuzma, et al.
Published: (2024)
Multi-Fidelity Machine Learning for Excited State Energies of Molecules
by: Vinod, Vivin, et al.
Published: (2023)
by: Vinod, Vivin, et al.
Published: (2023)
QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
by: Zariquiey, Francesc Sabanés, et al.
Published: (2025)
by: Zariquiey, Francesc Sabanés, et al.
Published: (2025)
ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures
by: Fan, Jinming, et al.
Published: (2026)
by: Fan, Jinming, et al.
Published: (2026)
Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)
by: Ji, Yajie, et al.
Published: (2025)
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)
Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)
by: Brozos, Christoforos, et al.
Published: (2024)
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
by: Pelaez, Raul P., et al.
Published: (2024)
by: Pelaez, Raul P., et al.
Published: (2024)
Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
by: Sanocki, Michal, et al.
Published: (2025)
by: Sanocki, Michal, et al.
Published: (2025)
MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
by: Osaro, Etinosa, et al.
Published: (2026)
by: Osaro, Etinosa, et al.
Published: (2026)
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026)
by: Baldwin, William J., et al.
Published: (2026)
Open-Source Fermionic Neural Networks with Ionic Charge Initialization
by: Pranesh, Shai, et al.
Published: (2024)
by: Pranesh, Shai, et al.
Published: (2024)
Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)
by: Pavšek, Jan, et al.
Published: (2026)
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
by: Scherbela, Michael, et al.
Published: (2025)
by: Scherbela, Michael, et al.
Published: (2025)
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
by: Gao, Nicholas, et al.
Published: (2024)
by: Gao, Nicholas, et al.
Published: (2024)
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
by: De Fabritiis, Gianni
Published: (2024)
by: De Fabritiis, Gianni
Published: (2024)
AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules
by: Farr, Stephen E., et al.
Published: (2026)
by: Farr, Stephen E., et al.
Published: (2026)
StringNET: Neural Network based Variational Method for Transition Pathways
by: Han, Jiayue, et al.
Published: (2024)
by: Han, Jiayue, et al.
Published: (2024)
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)
by: Brozos, Christoforos, et al.
Published: (2024)
Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)
by: Brozos, Christoforos, et al.
Published: (2024)
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
by: Hayer, Nicolas, et al.
Published: (2024)
by: Hayer, Nicolas, et al.
Published: (2024)
Learning Equivariant Non-Local Electron Density Functionals
by: Gao, Nicholas, et al.
Published: (2024)
by: Gao, Nicholas, 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)
Similar Items
-
OpenQDC: Open Quantum Data Commons
by: Gabellini, Cristian, et al.
Published: (2024) -
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
by: Fuchs, Paul, et al.
Published: (2024) -
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
by: Fuchs, Paul, et al.
Published: (2025) -
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
by: Kapuśniak, Kacper, et al.
Published: (2025) -
Implicit Neural Representations for Chemical Reaction Paths
by: Ramakrishnan, Kalyan, et al.
Published: (2025)