Copertina

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Liu, Xiaoqing, Yu, Xinyu, Wang, Yangshuai, Sun, Zhe-Tao, Luo, Zedong, Zeng, Kehan, Zhao, Teng, Bo, Shou-Hang, Xu, Zhenli
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
di: Liu, Xiaoqing, et al.
Pubblicazione: (2025)

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

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
di: Liu, Xiaoqing, et al.
Pubblicazione: (2025)

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

Machine learning interatomic potentials for solid-state precipitation
di: Piersante, Lorenzo, et al.
Pubblicazione: (2026)

The transformative capability of quantum-accurate machine learning interatomic potentials
di: Correa, Alfredo A., et al.
Pubblicazione: (2025)

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

Minimalist machine-learned interatomic potentials can predict complex structural behaviors accurately
di: Robredo-Magro, Iñigo, et al.
Pubblicazione: (2025)

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
di: Kang, Hanwen, et al.
Pubblicazione: (2025)

Controlling moving interfaces in solid state batteries
di: Mosleh, Salem, et al.
Pubblicazione: (2024)

Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
di: Ito, Kazuma
Pubblicazione: (2025)

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
di: Fellman, Aslak, et al.
Pubblicazione: (2024)

Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials
di: Adnan, Khalid Zobaid, et al.
Pubblicazione: (2024)

KLIFF: A framework to develop physics-based and machine learning interatomic potentials
di: Wen, Mingjian, et al.
Pubblicazione: (2021)

Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys
di: Wang, Jiantao, et al.
Pubblicazione: (2024)

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

Machine learning interatomic potentials for lithium battery electrolyte design
di: Gunwook Nam, et al.
Pubblicazione: (2026)

Beyond Adam: Disentangling Optimizer Effects in the Fine-Tuning of Atomistic Foundation Models
di: Liu, Xiaoqing, et al.
Pubblicazione: (2025)

Tuning the cathode/solid electrolyte interface for high‐performance solid‐state Na‐ion batteries
di: Raghunayakula Thirupathi, et al.
Pubblicazione: (2024)

vasp-mace: a VASP-style workflow interface for MACE machine-learning interatomic potentials
di: Grau-Crespo, Ricardo
Pubblicazione: (2026)

vasp-mace: a VASP-style workflow interface for MACE machine-learning interatomic potentials
di: Grau-Crespo, Ricardo
Pubblicazione: (2026)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
di: Kim, Dongjin, et al.
Pubblicazione: (2025)

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials
di: Ou, Yongliang, et al.
Pubblicazione: (2024)

Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
di: Zaverkin, Viktor, et al.
Pubblicazione: (2023)

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

Knowing when to trust machine-learned interatomic potentials
di: Mehdi, Shams, et al.
Pubblicazione: (2026)

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

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

Pushing the limits of unconstrained machine-learned interatomic potentials
di: Bigi, Filippo, et al.
Pubblicazione: (2026)

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

Coupled reaction and diffusion governing interface evolution in solid-state batteries
di: Ding, Jingxuan, et al.
Pubblicazione: (2025)

Combining Brillouin spectroscopy and machine learned interatomic potentials to probe mechanical properties of metal organic frameworks
di: Lindner, Florian P., et al.
Pubblicazione: (2024)

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

Self-consistent Coulomb interactions for machine learning interatomic potentials
di: Thomas, Jack, et al.
Pubblicazione: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
di: Cheng, Bingqing
Pubblicazione: (2024)

Lightweight equivariant model for efficient machine learning interatomic potentials
di: Yang, Ziduo, et al.
Pubblicazione: (2023)

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

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

Systematic assessment of various universal machine‐learning interatomic potentials
di: Haochen Yu, et al.
Pubblicazione: (2024)

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