MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
Fuente:
arXiv
Saved in:
| Main Authors: | Batatia, Ilyes, Baldwin, William J., Kuryla, Domantas, Hart, Joseph, Kasoar, Elliott, Elena, Alin M., Moore, Harry, Gawkowski, Mikołaj J., Shi, Benjamin X., Kapil, Venkat, Kourtis, Panagiotis, Magdău, Ioan-Bogdan, Csányi, Gábor |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
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)
Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., et al.
Published: (2024)
by: Niblett, Samuel P., 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)
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
by: Kuryla, Domantas, et al.
Published: (2025)
by: Kuryla, Domantas, et al.
Published: (2025)
The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
by: Gawkowski, Mikołaj J., et al.
Published: (2025)
by: Gawkowski, Mikołaj J., et al.
Published: (2025)
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials
by: Grega, Ivan, et al.
Published: (2024)
by: Grega, Ivan, et al.
Published: (2024)
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)
Equivariant Matrix Function Neural Networks
by: Batatia, Ilyes, et al.
Published: (2023)
by: Batatia, Ilyes, et al.
Published: (2023)
Zero Shot Molecular Generation via Similarity Kernels
by: Elijošius, Rokas, et al.
Published: (2024)
by: Elijošius, Rokas, et al.
Published: (2024)
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)
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
by: Karimitari, Nima, et al.
Published: (2026)
by: Karimitari, Nima, et al.
Published: (2026)
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
by: Firoz, Jesun, et al.
Published: (2025)
by: Firoz, Jesun, et al.
Published: (2025)
Computing solvation free energies of small molecules with experimental accuracy
by: Moore, J. Harry, et al.
Published: (2024)
by: Moore, J. Harry, et al.
Published: (2024)
Self-consistent Coulomb interactions for machine learning interatomic potentials
by: Thomas, Jack, et al.
Published: (2024)
by: Thomas, Jack, et al.
Published: (2024)
Symmetry Breaking in the Superionic Phase of Silver-Iodide
by: Hajibabaei, Amir, et al.
Published: (2024)
by: Hajibabaei, Amir, et al.
Published: (2024)
The Professional Educator: Fostering Teacher Leadership in North Syracuse
by: Kuryla, John
Published: (2019)
by: Kuryla, John
Published: (2019)
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)
Harmonic-to-anharmonic thermodynamic integration made simple using REG TI
by: Kapil, Venkat
Published: (2026)
by: Kapil, Venkat
Published: (2026)
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)
Resonances in reflective Hamiltonian Monte Carlo
by: Kroupa, Namu, et al.
Published: (2025)
by: Kroupa, Namu, et al.
Published: (2025)
Evaluation of Quality of Life in Patients with Chronic Leg Wounds: Can Platelet-rich Plasma Therapy Help?
by: Domantas Rainys
Published: (2020)
by: Domantas Rainys
Published: (2020)
Augusto Del Noce kaip postulatyvaus ateizmo kritikas
by: Domantas Markevičius
Published: (2020)
by: Domantas Markevičius
Published: (2020)
Guest Editorial: Special Topic on Software for Atomistic Machine Learning
by: Rupp, Matthias, et al.
Published: (2024)
by: Rupp, Matthias, et al.
Published: (2024)
Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion
by: Wang, Yangshuai, et al.
Published: (2025)
by: Wang, Yangshuai, et al.
Published: (2025)
Bond-Network Entropy Governs Heat Transport in Coordination-Disordered Solids
by: Iwanowski, Kamil, et al.
Published: (2024)
by: Iwanowski, Kamil, et al.
Published: (2024)
Performance of the MACE-MP-0 potential for calculating viscosity in LiF molten salt
by: Devereux, Harvey L., et al.
Published: (2024)
by: Devereux, Harvey L., et al.
Published: (2024)
In vitro comparison of accuracy between conventional and digital impression using elastomeric materials and two intra‐oral scanning devices
by: Eirini Palantza, et al.
Published: (2024)
by: Eirini Palantza, et al.
Published: (2024)
The table maker's quantum search
by: Kourtis, Stefanos
Published: (2026)
by: Kourtis, Stefanos
Published: (2026)
SpeechDx: A gold‐standard speech‐and‐language dataset for prognostic AD biomarker development
by: Lampros Kourtis
Published: (2025)
by: Lampros Kourtis
Published: (2025)
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)
Random Spin Committee Approach For Smooth Interatomic Potentials
by: Cărare, Vlad, et al.
Published: (2024)
by: Cărare, Vlad, et al.
Published: (2024)
Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
by: Nasir, Jamal Abdul, et al.
Published: (2024)
by: Nasir, Jamal Abdul, et al.
Published: (2024)
CEF: Connecting Elaborate Federal QKD Networks
by: Popa, Alin-Bogdan, et al.
Published: (2024)
by: Popa, Alin-Bogdan, et al.
Published: (2024)
Association of edentulism and obstructive sleep apnea: A systematic review
by: Aspasia Pachiou, et al.
Published: (2024)
by: Aspasia Pachiou, et al.
Published: (2024)
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites
by: Karimitari, Nima, et al.
Published: (2024)
by: Karimitari, Nima, et al.
Published: (2024)
A‐ MACE ‐Ing: A Real‐World Analysis of Semaglutide's Impact on Major Adverse Cardiovascular Events ( MACE ) in Patients With Hidradenitis Suppurativa
by: Rahib K. Islam, et al.
Published: (2025)
by: Rahib K. Islam, et al.
Published: (2025)
Integrating External Tools with Large Language Models to Improve Accuracy
by: Niketan, Nripesh, et al.
Published: (2025)
by: Niketan, Nripesh, et al.
Published: (2025)
Thermal Conductivity Predictions with Foundation Atomistic Models
by: Póta, Balázs, et al.
Published: (2024)
by: Póta, Balázs, et al.
Published: (2024)
Two dimensional sub-wavelength topological dark state lattices
by: Burba, Domantas, et al.
Published: (2025)
by: Burba, Domantas, et al.
Published: (2025)
Similar Items
-
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
by: Kovács, Dávid Péter, et al.
Published: (2023) -
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026) -
Transferability of datasets between Machine-Learning Interaction Potentials
by: Niblett, Samuel P., 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) -
How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
by: Kuryla, Domantas, et al.
Published: (2025)