Saved in:
| Main Authors: | Kumar, Nitesh, Lai, Jianwei, Mezerkor, Casey S., Wang, Jiaqi, Wiaderek, Kamila M., Bazak, J. David, Blau, Samuel M., Crumlin, Ethan J. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.20183 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
by: Levine, Daniel S., et al.
Published: (2025)
by: Levine, Daniel S., et al.
Published: (2025)
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)
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)
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)
MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform
by: Chiang, Yuan, et al.
Published: (2025)
by: Chiang, Yuan, et al.
Published: (2025)
Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate
by: Soyemi, Ademola, et al.
Published: (2025)
by: Soyemi, Ademola, et al.
Published: (2025)
Machine-Learned Potentials for Solvation Modeling
by: Banchode, Roopshree, et al.
Published: (2025)
by: Banchode, Roopshree, 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)
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)
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)
Geometry-enhanced Pre-training on Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2023)
by: Cui, Taoyong, et al.
Published: (2023)
MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)
by: Wehrhan, Leon, et al.
Published: (2025)
Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)
by: Chong, Sanggyu, et al.
Published: (2025)
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
by: Matin, Sakib, et al.
Published: (2025)
by: Matin, Sakib, et al.
Published: (2025)
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
by: Karimitari, Nima, et al.
Published: (2026)
by: Karimitari, Nima, 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)
Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding
by: Wu, Guanchen, et al.
Published: (2026)
by: Wu, Guanchen, et al.
Published: (2026)
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
by: Maxson, Tristan, et al.
Published: (2024)
by: Maxson, Tristan, et al.
Published: (2024)
Benchmarking Universal Interatomic Potentials on Zeolite Structures
by: Ito, Shusuke, et al.
Published: (2025)
by: Ito, Shusuke, et al.
Published: (2025)
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)
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)
Random Spin Committee Approach For Smooth Interatomic Potentials
by: Cărare, Vlad, et al.
Published: (2024)
by: Cărare, Vlad, et al.
Published: (2024)
Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
by: Dominguez, Jon Eunan Quinlivan, et al.
Published: (2025)
by: Dominguez, Jon Eunan Quinlivan, et al.
Published: (2025)
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)
Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)
by: Liu, Yuzhi, 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)
MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
by: Osaro, Etinosa, et al.
Published: (2026)
by: Osaro, Etinosa, 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)
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs
by: Boiko, Daniil A., et al.
Published: (2024)
by: Boiko, Daniil A., et al.
Published: (2024)
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)
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)
Shoot from the HIP: Hessian Interatomic Potentials without derivatives
by: Burger, Andreas, et al.
Published: (2025)
by: Burger, Andreas, et al.
Published: (2025)
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)
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)
Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials
by: Dutta, Soumajit, et al.
Published: (2025)
by: Dutta, Soumajit, et al.
Published: (2025)
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)
Similar Items
-
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
by: Levine, Daniel S., et al.
Published: (2025) -
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026) -
Predicting Spectroscopic Properties of Solvated Nile Red with Automated Workflows for Machine Learned Interatomic Potentials
by: Eller, Jacob, et al.
Published: (2025) -
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026) -
MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform
by: Chiang, Yuan, et al.
Published: (2025)