Pull-off force prediction in viscoelastic adhesive Hertzian contact by physics augmented machine learning
Fuente:
arXiv
Saved in:
| Main Authors: | Maghami, Ali, Stender, Merten, Papangelo, Antonio |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Viscoelastic amplification of the pull-off stress in the detachment of a rigid flat punch from an adhesive soft viscoelastic layer
by: Maghami, Ali, et al.
Published: (2023)
by: Maghami, Ali, et al.
Published: (2023)
Bulk and fracture process zone contribution to the rate-dependent adhesion amplification in viscoelastic broad-band materials
by: Maghami, Ali, et al.
Published: (2024)
by: Maghami, Ali, et al.
Published: (2024)
A predictive machine learning force field framework for liquid electrolyte development
by: Gong, Sheng, et al.
Published: (2024)
by: Gong, Sheng, et al.
Published: (2024)
Dynamically training machine-learning-based force fields for strongly anharmonic materials
by: Callsen, Martin, et al.
Published: (2026)
by: Callsen, Martin, et al.
Published: (2026)
Insight into the effect of force error on the thermal conductivity from machine-learned potentials
by: Zhou, Wenjiang, et al.
Published: (2024)
by: Zhou, Wenjiang, et al.
Published: (2024)
Temperature dependent ferroelectricity in strained KTaO3 with machine learned force field
by: Zhu, Yu, et al.
Published: (2025)
by: Zhu, Yu, et al.
Published: (2025)
Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
by: CampBell, Audrey, et al.
Published: (2024)
by: CampBell, Audrey, et al.
Published: (2024)
On-the-fly machine learning-augmented constrained AIMD to design new routes from glassy carbon to quenchable amorphous diamond with low pressure and temperature
by: Cheng, Meng-Qi, et al.
Published: (2025)
by: Cheng, Meng-Qi, et al.
Published: (2025)
Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models
by: Wang, Ruoyu, et al.
Published: (2025)
by: Wang, Ruoyu, et al.
Published: (2025)
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
by: Solovykh, A. A., et al.
Published: (2025)
by: Solovykh, A. A., et al.
Published: (2025)
How unconstrained machine-learning models learn physical symmetries
by: Domina, Michelangelo, et al.
Published: (2026)
by: Domina, Michelangelo, et al.
Published: (2026)
Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields
by: Grunert, Malte, et al.
Published: (2025)
by: Grunert, Malte, et al.
Published: (2025)
Cyclic and helical symmetry-informed machine learned force fields: Application to lattice vibrations in carbon nanotubes
by: Sharma, Abhiraj, et al.
Published: (2024)
by: Sharma, Abhiraj, et al.
Published: (2024)
Shotgun crystal structure prediction using machine-learned formation energies
by: Liu, Chang, et al.
Published: (2023)
by: Liu, Chang, et al.
Published: (2023)
GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling
by: Haldar, Anubhab, et al.
Published: (2025)
by: Haldar, Anubhab, et al.
Published: (2025)
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
by: Baghishov, Ilgar, et al.
Published: (2025)
by: Baghishov, Ilgar, et al.
Published: (2025)
Node pruning reveals compact and optimal substructures within large networks
by: Yadav, Manish, et al.
Published: (2025)
by: Yadav, Manish, et al.
Published: (2025)
Pull-off strength of mushroom-shaped fibrils adhered to rigid substrates
by: Betegón, C., et al.
Published: (2025)
by: Betegón, C., et al.
Published: (2025)
PINK: physical-informed machine learning for lattice thermal conductivity
by: Liu, Yujie, et al.
Published: (2025)
by: Liu, Yujie, et al.
Published: (2025)
Transferable empirical pseudopotenials from machine learning
by: Kim, Rokyeon, et al.
Published: (2023)
by: Kim, Rokyeon, et al.
Published: (2023)
Ultra-fast interpretable machine-learning potentials
by: Xie, Stephen R., et al.
Published: (2021)
by: Xie, Stephen R., et al.
Published: (2021)
Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning
by: Yamazaki, Shuya, et al.
Published: (2025)
by: Yamazaki, Shuya, et al.
Published: (2025)
Efficient dataset generation for machine learning perovskite alloys
by: Homm, Henrietta, et al.
Published: (2025)
by: Homm, Henrietta, et al.
Published: (2025)
Understanding solid nitrogen through machine learning simulation
by: Kirsz, Marcin, et al.
Published: (2024)
by: Kirsz, Marcin, et al.
Published: (2024)
Point defect formation at finite temperatures with machine learning force fields
by: Mosquera-Lois, Irea, et al.
Published: (2024)
by: Mosquera-Lois, Irea, et al.
Published: (2024)
Electrostatic interactions in atomistic and machine-learned potentials for polar materials
by: Monacelli, Lorenzo, et al.
Published: (2024)
by: Monacelli, Lorenzo, et al.
Published: (2024)
Constructing multicomponent cluster expansions with machine-learning and chemical embedding
by: Müller, Yann L., et al.
Published: (2024)
by: Müller, Yann L., et al.
Published: (2024)
Neural operator accelerated atomistic to continuum concurrent multiscale simulations of viscoelasticity
by: Sohail, Tanvir, et al.
Published: (2026)
by: Sohail, Tanvir, et al.
Published: (2026)
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
by: Klimanova, Olga, et al.
Published: (2024)
by: Klimanova, Olga, et al.
Published: (2024)
Efficient construction of effective Hamiltonians with a hybrid machine learning method
by: Cheng, Yang, et al.
Published: (2025)
by: Cheng, Yang, et al.
Published: (2025)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure
by: Fantasia, Andrea, et al.
Published: (2025)
by: Fantasia, Andrea, et al.
Published: (2025)
General-purpose machine-learned potential for 16 elemental metals and their alloys
by: Song, Keke, et al.
Published: (2023)
by: Song, Keke, et al.
Published: (2023)
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)
by: Byggmästar, Jesper, et al.
Published: (2026)
Deep learning generative model for crystal structure prediction
by: Luo, Xiaoshan, et al.
Published: (2024)
by: Luo, Xiaoshan, et al.
Published: (2024)
Classification analysis of transition-metal chalcogenides and oxides using quantum machine learning
by: Vedavyasa, Kurudi V, et al.
Published: (2024)
by: Vedavyasa, Kurudi V, et al.
Published: (2024)
What can machine learning help with microstructure-informed materials modeling and design?
by: Peng, Xiang-Long, et al.
Published: (2024)
by: Peng, Xiang-Long, et al.
Published: (2024)
Composition design of refractory compositionally complex alloys using machine learning models
by: Liang, Tao, et al.
Published: (2026)
by: Liang, Tao, et al.
Published: (2026)
Exploring high thermal conductivity polymers via interpretable machine learning with physical descriptors
by: Huang, Xiang, et al.
Published: (2023)
by: Huang, Xiang, et al.
Published: (2023)
A machine-learning framework for accelerating spin-lattice relaxation simulations
by: Briganti, Valerio, et al.
Published: (2024)
by: Briganti, Valerio, et al.
Published: (2024)
Similar Items
-
Viscoelastic amplification of the pull-off stress in the detachment of a rigid flat punch from an adhesive soft viscoelastic layer
by: Maghami, Ali, et al.
Published: (2023) -
Bulk and fracture process zone contribution to the rate-dependent adhesion amplification in viscoelastic broad-band materials
by: Maghami, Ali, et al.
Published: (2024) -
A predictive machine learning force field framework for liquid electrolyte development
by: Gong, Sheng, et al.
Published: (2024) -
Dynamically training machine-learning-based force fields for strongly anharmonic materials
by: Callsen, Martin, et al.
Published: (2026) -
Insight into the effect of force error on the thermal conductivity from machine-learned potentials
by: Zhou, Wenjiang, et al.
Published: (2024)