Imagen de Portada

Fine-tuning universal machine learning potentials for transition state search in surface catalysis

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Cheula, Raffaele, Andersen, Mie, Kitchin, John R.
Formato:	Preprint
Publicado:	2026
Materias:	Materials Science
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
por: Kempen, Luuk H. E., et al.
Publicado: (2025)

Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
por: Cheula, Raffaele, et al.
Publicado: (2025)

Interpretable machine learned predictions of adsorption energies at the metal--oxide interface
por: Nielsen, Marius Juul, et al.
Publicado: (2025)

Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous Catalysis
por: Wander, Brook, et al.
Publicado: (2024)

Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
por: Zhou, Chun, et al.
Publicado: (2026)

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
por: Kim, Jisu, et al.
Publicado: (2025)

AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis
por: Allam, Omar, et al.
Publicado: (2025)

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
por: Deng, Bowen, et al.
Publicado: (2024)

Performance of universal machine learning potentials in global optimization
por: Marcial, Edan T., et al.
Publicado: (2026)

Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
por: Kempen, Luuk H. E., et al.
Publicado: (2025)

Systematic assessment of various universal machine-learning interatomic potentials
por: Yu, Haochen, et al.
Publicado: (2024)

Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning
por: Radova, Mariia, et al.
Publicado: (2025)

Atomic-scale phase-field modeling with universal machine learning potentials
por: Masuda, Kairi, et al.
Publicado: (2025)

Cross-functional transferability in universal machine learning interatomic potentials
por: Huang, Xu, et al.
Publicado: (2025)

Electronic structure and elasticity of the Ta-W solid solution
por: Abdelmaqsoud, Kareem, et al.
Publicado: (2026)

Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
por: Klimanova, Olga, et al.
Publicado: (2024)

A universal machine learning model for the electronic density of states
por: How, Wei Bin, et al.
Publicado: (2025)

Screening of material defects using universal machine-learning interatomic potentials
por: Berger, Ethan, et al.
Publicado: (2025)

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
por: Liu, Xiaoqing, et al.
Publicado: (2026)

Polynomial machine learning potential and its application to global structure search in the ternary Cu-Ag-Au alloy
por: Seko, Atsuto
Publicado: (2024)

Comparing the latent features of universal machine-learning interatomic potentials
por: Chorna, Sofiia, et al.
Publicado: (2025)

Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
por: Kellner, Matthias, et al.
Publicado: (2026)

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials
por: Liu, Xin, et al.
Publicado: (2026)

Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
por: Wakai, Hayato, et al.
Publicado: (2024)

Environment-adaptive machine learning potentials
por: Nguyen, Ngoc Cuong, et al.
Publicado: (2024)

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
por: Noordhoek, Kyle, et al.
Publicado: (2023)

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
por: Ju, Suyeon, et al.
Publicado: (2025)

Ultra-fast interpretable machine-learning potentials
por: Xie, Stephen R., et al.
Publicado: (2021)

Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials
por: Wakai, Hayato, et al.
Publicado: (2025)

Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models
por: Wang, Ruoyu, et al.
Publicado: (2025)

Platonic representation of foundation machine learning interatomic potentials
por: Li, Zhenzhu, et al.
Publicado: (2025)

Cartesian atomic moment machine learning interatomic potentials
por: Wen, Mingjian, et al.
Publicado: (2024)

Benchmarking phonon anharmonicity in machine learning interatomic potentials
por: Bandi, Sasaank, et al.
Publicado: (2024)

Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
por: Dicks, Oliver A., et al.
Publicado: (2025)

Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials
por: Casillas-Trujillo, Luis, et al.
Publicado: (2024)

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
por: Kuner, Matthew C., et al.
Publicado: (2025)

Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon
por: Wakai, Hayato, et al.
Publicado: (2025)

Practical and accurate density functionals for transition-metal heterogeneous catalysis
por: Shi, Benjamin X., et al.
Publicado: (2026)

Toward machine learning interatomic potentials for modeling uranium mononitride
por: Alzate-Vargas, Lorena, et al.
Publicado: (2024)

Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances
por: Musielewicz, Joseph, et al.
Publicado: (2024)