Copertina

Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Ruoyu, Gao, Yuxiang, Wu, Hongyu, Zhong, Zhicheng
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Uncovering coupled ionic-polaronic dynamics and interfacial enhancement in Li$_x$FePO$_4$
di: Xie, Fengyu, et al.
Pubblicazione: (2025)

Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields
di: Grunert, Malte, et al.
Pubblicazione: (2025)

Path-integral molecular dynamics with actively-trained and universal machine learning force fields
di: Solovykh, A. A., et al.
Pubblicazione: (2025)

Chemical-space completeness: a new strategy for crystalline materials exploration
di: Xie, Fengyu, et al.
Pubblicazione: (2025)

Dynamically training machine-learning-based force fields for strongly anharmonic materials
di: Callsen, Martin, et al.
Pubblicazione: (2026)

DPmoire: A tool for constructing accurate machine learning force fields in moiré systems
di: Liu, Jiaxuan, et al.
Pubblicazione: (2024)

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries
di: Alghamdi, Nada, et al.
Pubblicazione: (2025)

T-MSD: An improved method for ionic diffusion coefficient calculation from molecular dynamics
di: Gao, Yuxiang, et al.
Pubblicazione: (2025)

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

A predictive machine learning force field framework for liquid electrolyte development
di: Gong, Sheng, et al.
Pubblicazione: (2024)

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

Point defect formation at finite temperatures with machine learning force fields
di: Mosquera-Lois, Irea, et al.
Pubblicazione: (2024)

Mechanism of Anisotropic Crystallization and Phase Transitions under Van der Waals Squeezing
di: Gao, Yuxiang, et al.
Pubblicazione: (2025)

Fine tuning generative adversarial networks with universal force fields: application to two-dimensional topological insulators
di: Tyner, Alexander C.
Pubblicazione: (2025)

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

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture
di: Brabson, Logan M., et al.
Pubblicazione: (2025)

Spontaneous curvature in two-dimensional van der Waals heterostructures
di: Gao, Yuxiang, et al.
Pubblicazione: (2024)

Temperature dependent ferroelectricity in strained KTaO3 with machine learned force field
di: Zhu, Yu, et al.
Pubblicazione: (2025)

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

Insight into the effect of force error on the thermal conductivity from machine-learned potentials
di: Zhou, Wenjiang, et al.
Pubblicazione: (2024)

Undamped Soliton-like Domain Wall Motion in Sliding Ferroelectrics
di: Shi, Yubai, et al.
Pubblicazione: (2025)

Cyclic and helical symmetry-informed machine learned force fields: Application to lattice vibrations in carbon nanotubes
di: Sharma, Abhiraj, et al.
Pubblicazione: (2024)

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

GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling
di: Haldar, Anubhab, et al.
Pubblicazione: (2025)

Deep learning generative model for crystal structure prediction
di: Luo, Xiaoshan, et al.
Pubblicazione: (2024)

Efficient dataset generation for machine learning perovskite alloys
di: Homm, Henrietta, et al.
Pubblicazione: (2025)

Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
di: Wu, Xiguang, et al.
Pubblicazione: (2024)

Differentiable hybrid force fields support scalable autonomous electrolyte discovery
di: Wang, Xintian, et al.
Pubblicazione: (2026)

Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
di: Kaur, Harveen, et al.
Pubblicazione: (2024)

High-quality, high-information datasets for universal atomistic machine learning
di: Malosso, Cesare, et al.
Pubblicazione: (2026)

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

Fine-tuning universal machine learning potentials for transition state search in surface catalysis
di: Cheula, Raffaele, et al.
Pubblicazione: (2026)

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

Efficient construction of effective Hamiltonians with a hybrid machine learning method
di: Cheng, Yang, et al.
Pubblicazione: (2025)

Pull-off force prediction in viscoelastic adhesive Hertzian contact by physics augmented machine learning
di: Maghami, Ali, et al.
Pubblicazione: (2025)

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

Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
di: Mazitov, Arslan, et al.
Pubblicazione: (2025)

Decoding lithium's subtle phase stability with a machine learning force field
di: Shen, Yiheng, et al.
Pubblicazione: (2025)

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

A foundation machine learning potential with polarizable long-range interactions for materials modelling
di: Gao, Rongzhi, et al.
Pubblicazione: (2024)