Cover Image

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Dominguez, Jon Eunan Quinlivan, Christiansen, Mads-Peter Verner, Neyman, Konstantin M., Hammer, Bøjrk, Bruix, Albert
Format:	Preprint
Published:	2025
Subjects:	Materials Science Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
by: Farris, Riccardo, et al.
Published: (2025)

Efficient ensemble uncertainty estimation in Gaussian Processes Regression
by: Christiansen, Mads-Peter Verner, et al.
Published: (2024)

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
by: Brunken, Christoph, 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)

Geometry-enhanced Pre-training on Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2023)

Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
by: Zhang, Yaolong, et al.
Published: (2025)

Reliable and Efficient Automated Transition-State Searches with Machine-Learned Interatomic Potentials
by: Marks, Jonah, et al.
Published: (2026)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, 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)

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
by: Brunken, Christoph, et al.
Published: (2026)

MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)

False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

A bivariational, stable and convergent hierarchy for time-dependent coupled cluster with adaptive basis sets
by: Højlund, Mads Greisen, et al.
Published: (2024)

DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
by: Midgley, Laurence I., et al.
Published: (2026)

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
by: Matin, Sakib, et al.
Published: (2025)

Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy
by: Hänseroth, Jonas, 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)

Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
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)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2025)

Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
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)

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026)

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
by: Christiansen, Mads-Peter Verner, et al.
Published: (2025)

$Δ$-model correction of Foundation Model based on the models own understanding
by: Christiansen, Mads-Peter Verner, 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)

Active Δ-learning with universal potentials for global structure optimization
by: Pitfield, Joe, et al.
Published: (2025)

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, 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)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
by: Fu, Cong, et al.
Published: (2025)

Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations
by: Hänseroth, Jonas, et al.
Published: (2026)

Cascading symmetry constraint during machine learning-enabled structural search for sulfur induced Cu(111)-$(\sqrt{43}\times\sqrt{43})$ surface reconstruction
by: Brix, Florian, et al.
Published: (2025)

Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials
by: Chahal, Rajni, et al.
Published: (2024)

Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
by: Edwards, Connor W., et al.
Published: (2026)

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate
by: Soyemi, Ademola, et al.
Published: (2025)

Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
by: Karimitari, Nima, et al.
Published: (2026)