Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Baldwin, William J., Batatia, Ilyes, Vondrák, Martin, Margraf, Johannes T., Csányi, Gábor |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
by: Batatia, Ilyes, et al.
Published: (2026)
by: Batatia, Ilyes, et al.
Published: (2026)
Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials
by: Pracht, Philipp, et al.
Published: (2024)
by: Pracht, Philipp, et al.
Published: (2024)
Zero Shot Molecular Generation via Similarity Kernels
by: Elijošius, Rokas, et al.
Published: (2024)
by: Elijošius, Rokas, et al.
Published: (2024)
Equivariant Matrix Function Neural Networks
by: Batatia, Ilyes, et al.
Published: (2023)
by: Batatia, Ilyes, et al.
Published: (2023)
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)
by: Parker, Isaac J., 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)
Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)
by: Liu, Yuzhi, et al.
Published: (2026)
MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
by: Osaro, Etinosa, et al.
Published: (2026)
by: Osaro, Etinosa, et al.
Published: (2026)
Random Spin Committee Approach For Smooth Interatomic Potentials
by: Cărare, Vlad, et al.
Published: (2024)
by: Cărare, Vlad, et al.
Published: (2024)
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)
by: Maruf, Moin Uddin, et al.
Published: (2025)
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
by: Schaaf, Lars L., et al.
Published: (2024)
by: Schaaf, Lars L., et al.
Published: (2024)
Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields
by: Batatia, Ilyes, et al.
Published: (2025)
by: Batatia, Ilyes, et al.
Published: (2025)
Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
by: Rodriguez, Austin, et al.
Published: (2026)
by: Rodriguez, Austin, et al.
Published: (2026)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)
by: Stark, Wojciech G., et al.
Published: (2024)
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
by: Midgley, Laurence I., et al.
Published: (2026)
by: Midgley, Laurence I., et al.
Published: (2026)
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
by: Warford, Thomas, et al.
Published: (2026)
by: Warford, Thomas, 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)
Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
by: Lam, Terry C. W., et al.
Published: (2026)
by: Lam, Terry C. W., et al.
Published: (2026)
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)
by: Liu, Ryan, et al.
Published: (2026)
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
by: Karimitari, Nima, et al.
Published: (2026)
by: Karimitari, Nima, et al.
Published: (2026)
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
by: Takamoto, Makoto, et al.
Published: (2024)
by: Takamoto, Makoto, 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)
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)
Shoot from the HIP: Hessian Interatomic Potentials without derivatives
by: Burger, Andreas, et al.
Published: (2025)
by: Burger, Andreas, et al.
Published: (2025)
Guest Editorial: Special Topic on Software for Atomistic Machine Learning
by: Rupp, Matthias, et al.
Published: (2024)
by: Rupp, Matthias, et al.
Published: (2024)
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
by: Grega, Ivan, et al.
Published: (2024)
by: Grega, Ivan, et al.
Published: (2024)
Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)
by: Niblett, Samuel P., et al.
Published: (2024)
Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)
by: Rittig, Jan G., et al.
Published: (2025)
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
by: Kaur, Harveen, et al.
Published: (2024)
by: Kaur, Harveen, et al.
Published: (2024)
Enhanced Representation-Based Sampling for the Efficient Generation of Datasets for Machine-Learned Interatomic Potentials
by: Schäfer, Moritz René, et al.
Published: (2026)
by: Schäfer, Moritz René, 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)
Machine learning for accuracy in density functional approximations
by: Voss, Johannes
Published: (2023)
by: Voss, Johannes
Published: (2023)
Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
by: Schäfer, Moritz René, et al.
Published: (2025)
by: Schäfer, Moritz René, et al.
Published: (2025)
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)
Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
by: De Fabritiis, Gianni
Published: (2024)
by: De Fabritiis, Gianni
Published: (2024)
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)
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)
by: Ho, Cheuk Hin, et al.
Published: (2025)
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
by: Shao, Shihao, et al.
Published: (2024)
by: Shao, Shihao, et al.
Published: (2024)
Self-consistent Validation for Machine Learning Electronic Structure
by: Hu, Gengyuan, et al.
Published: (2024)
by: Hu, Gengyuan, et al.
Published: (2024)
MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)
by: Wehrhan, Leon, et al.
Published: (2025)
Similar Items
-
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
by: Batatia, Ilyes, et al.
Published: (2026) -
Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials
by: Pracht, Philipp, et al.
Published: (2024) -
Zero Shot Molecular Generation via Similarity Kernels
by: Elijošius, Rokas, et al.
Published: (2024) -
Equivariant Matrix Function Neural Networks
by: Batatia, Ilyes, et al.
Published: (2023) -
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)