Reliable and Efficient Automated Transition-State Searches with Machine-Learned Interatomic Potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Marks, Jonah, Vandezande, Jonathon, Gomes, Joseph |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
The vDZP Basis Set Is Effective For Many Density Functionals
by: Wagen, Corin C., et al.
Published: (2024)
by: Wagen, Corin C., et al.
Published: (2024)
Incorporation of Internal Coordinates Interpolation into the Freezing String Method
by: Marks, Jonah, et al.
Published: (2024)
by: Marks, Jonah, et al.
Published: (2024)
Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation
by: Mann, Elias L., et al.
Published: (2025)
by: Mann, Elias L., et al.
Published: (2025)
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)
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)
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)
by: Ho, Cheuk Hin, et al.
Published: (2025)
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)
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)
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)
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)
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)
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)
Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)
by: Liu, Yuzhi, et al.
Published: (2026)
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)
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)
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)
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)
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)
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)
HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2025)
by: Cui, Taoyong, et al.
Published: (2025)
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)
by: Kuroda, Naoya, et al.
Published: (2026)
Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
by: Ko, Tsz Wai, et al.
Published: (2024)
by: Ko, Tsz Wai, et al.
Published: (2024)
Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials
by: Chahal, Rajni, et al.
Published: (2024)
by: Chahal, Rajni, et al.
Published: (2024)
Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
by: Fu, Cong, et al.
Published: (2025)
by: Fu, Cong, et al.
Published: (2025)
Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
by: Edwards, Connor W., et al.
Published: (2026)
by: Edwards, Connor W., 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)
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)
Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy
by: Hänseroth, Jonas, et al.
Published: (2025)
by: Hänseroth, Jonas, 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)
Locating Ab Initio Transition States via Approximate Geodesics on Machine Learned Potential Energy Surfaces
by: Hait, Diptarka, et al.
Published: (2025)
by: Hait, Diptarka, et al.
Published: (2025)
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)
Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration
by: Xu, Jiayan, et al.
Published: (2025)
by: Xu, Jiayan, 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)
Similar Items
-
Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials
by: Marks, Jonah, et al.
Published: (2025) -
The vDZP Basis Set Is Effective For Many Density Functionals
by: Wagen, Corin C., et al.
Published: (2024) -
Incorporation of Internal Coordinates Interpolation into the Freezing String Method
by: Marks, Jonah, et al.
Published: (2024) -
Egret-1: Pretrained Neural Network Potentials for Efficient and Accurate Bioorganic Simulation
by: Mann, Elias L., et al.
Published: (2025) -
Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
by: Zhang, Yaolong, et al.
Published: (2025)