Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Chong, Sanggyu, Jiang, Tong, Domina, Michelangelo, Bigi, Filippo, Grasselli, Federico, Lee, Joonho, Ceriotti, Michele |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Representing spherical tensors with scalar-based machine-learning models
by: Domina, Michelangelo, et al.
Published: (2025)
by: Domina, Michelangelo, et al.
Published: (2025)
Prediction rigidities for data-driven chemistry
by: Chong, Sanggyu, et al.
Published: (2024)
by: Chong, Sanggyu, et al.
Published: (2024)
FlashMD: long-stride, universal prediction of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)
by: Domina, Michelangelo, et al.
Published: (2026)
Robustness of Local Predictions in Atomistic Machine Learning Models
by: Chong, Sanggyu, et al.
Published: (2023)
by: Chong, Sanggyu, et al.
Published: (2023)
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
by: Bigi, Filippo, et al.
Published: (2024)
by: Bigi, Filippo, et al.
Published: (2024)
Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)
by: How, Wei Bin, et al.
Published: (2024)
Learning the action for long-time-step simulations of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2024)
by: Bigi, Filippo, et al.
Published: (2024)
A universal machine learning model for the electronic density of states
by: How, Wei Bin, et al.
Published: (2025)
by: How, Wei Bin, et al.
Published: (2025)
Uncertainty in the era of machine learning for atomistic modeling
by: Grasselli, Federico, et al.
Published: (2025)
by: Grasselli, Federico, et al.
Published: (2025)
A general formalism for machine-learning models based on multipolar-spherical harmonics
by: Domina, Michelangelo, et al.
Published: (2025)
by: Domina, Michelangelo, et al.
Published: (2025)
Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)
by: Bigi, Filippo, et al.
Published: (2026)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2025)
by: Bigi, Filippo, et al.
Published: (2025)
Atom-Density Representations for Machine Learning
by: Willatt, Michael J., et al.
Published: (2018)
by: Willatt, Michael J., et al.
Published: (2018)
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)
by: Ho, Cheuk Hin, et al.
Published: (2025)
MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)
by: Wehrhan, Leon, et al.
Published: (2025)
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)
High-quality, high-information datasets for universal atomistic machine learning
by: Malosso, Cesare, et al.
Published: (2026)
by: Malosso, Cesare, et al.
Published: (2026)
Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
by: Musil, Felix, et al.
Published: (2018)
by: Musil, Felix, et al.
Published: (2018)
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)
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
by: Matin, Sakib, et al.
Published: (2025)
by: Matin, Sakib, et al.
Published: (2025)
Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)
by: Ji, Yajie, et al.
Published: (2025)
Uncertainty quantification by direct propagation of shallow ensembles
by: Kellner, Matthias, et al.
Published: (2024)
by: Kellner, Matthias, et al.
Published: (2024)
Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials
by: Sofiia Chorna, et al.
Published: (2026)
by: Sofiia Chorna, et al.
Published: (2026)
Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)
by: Liu, Yuzhi, et al.
Published: (2026)
Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
by: Zhang, Yaolong, et al.
Published: (2025)
by: Zhang, Yaolong, et al.
Published: (2025)
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)
Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins
by: Zeng, Lejia, et al.
Published: (2026)
by: Zeng, Lejia, et al.
Published: (2026)
Reliable and Efficient Automated Transition-State Searches with Machine-Learned Interatomic Potentials
by: Marks, Jonah, et al.
Published: (2026)
by: Marks, Jonah, et al.
Published: (2026)
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)
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)
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)
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
by: Brunken, Christoph, et al.
Published: (2025)
by: Brunken, Christoph, et al.
Published: (2025)
Predicting Spectroscopic Properties of Solvated Nile Red with Automated Workflows for Machine Learned Interatomic Potentials
by: Eller, Jacob, et al.
Published: (2025)
by: Eller, Jacob, et al.
Published: (2025)
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
by: Brunken, Christoph, et al.
Published: (2026)
by: Brunken, Christoph, et al.
Published: (2026)
Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
by: Hooven, Natalie E., et al.
Published: (2025)
by: Hooven, Natalie E., et al.
Published: (2025)
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)
by: Mazitov, Arslan, et al.
Published: (2025)
Similar Items
-
Representing spherical tensors with scalar-based machine-learning models
by: Domina, Michelangelo, et al.
Published: (2025) -
Prediction rigidities for data-driven chemistry
by: Chong, Sanggyu, et al.
Published: (2024) -
FlashMD: long-stride, universal prediction of molecular dynamics
by: Bigi, Filippo, et al.
Published: (2025) -
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026) -
Robustness of Local Predictions in Atomistic Machine Learning Models
by: Chong, Sanggyu, et al.
Published: (2023)