Conservative adaptive-precision interatomic potentials
Fuente:
arXiv
Guardado en:
| Autores principales: | Immel, David, Drautz, Ralf, Sutmann, Godehard |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Adaptive-precision potentials for large-scale atomistic simulations
por: Immel, David, et al.
Publicado: (2024)
por: Immel, David, et al.
Publicado: (2024)
Nanoindentation simulations for copper and tungsten with adaptive-precision potentials
por: Immel, David, et al.
Publicado: (2025)
por: Immel, David, et al.
Publicado: (2025)
Graph atomic cluster expansion for foundational machine learning interatomic potentials
por: Lysogorskiy, Yury, et al.
Publicado: (2025)
por: Lysogorskiy, Yury, et al.
Publicado: (2025)
Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
por: Bochkarev, Anton, et al.
Publicado: (2026)
por: Bochkarev, Anton, et al.
Publicado: (2026)
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
por: Wang, Weishi, et al.
Publicado: (2025)
por: Wang, Weishi, et al.
Publicado: (2025)
Repulsive interatomic potentials calculated at three levels of theory
por: Nordlund, Kai, et al.
Publicado: (2025)
por: Nordlund, Kai, et al.
Publicado: (2025)
The transformative capability of quantum-accurate machine learning interatomic potentials
por: Correa, Alfredo A., et al.
Publicado: (2025)
por: Correa, Alfredo A., et al.
Publicado: (2025)
High-performance training and inference for deep equivariant interatomic potentials
por: Tan, Chuin Wei, et al.
Publicado: (2025)
por: Tan, Chuin Wei, et al.
Publicado: (2025)
Cartesian atomic cluster expansion for machine learning interatomic potentials
por: Cheng, Bingqing
Publicado: (2024)
por: Cheng, Bingqing
Publicado: (2024)
Machine learning interatomic potential can infer electrical response
por: Zhong, Peichen, et al.
Publicado: (2025)
por: Zhong, Peichen, et al.
Publicado: (2025)
Machine-learning interatomic potential for AlN for epitaxial simulation
por: Taormina, Nicholas, et al.
Publicado: (2025)
por: Taormina, Nicholas, et al.
Publicado: (2025)
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
por: Zaverkin, Viktor, et al.
Publicado: (2023)
por: Zaverkin, Viktor, et al.
Publicado: (2023)
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
por: Sharma, Kartikeya, et al.
Publicado: (2025)
por: Sharma, Kartikeya, et al.
Publicado: (2025)
Suitability of available interatomic potentials for Sn to model its 2D allotropes
por: Maździarz, Marcin
Publicado: (2024)
por: Maździarz, Marcin
Publicado: (2024)
Screening of material defects using universal machine-learning interatomic potentials
por: Berger, Ethan, et al.
Publicado: (2025)
por: Berger, Ethan, et al.
Publicado: (2025)
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
por: Byggmästar, Jesper, et al.
Publicado: (2026)
por: Byggmästar, Jesper, et al.
Publicado: (2026)
Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
por: Bhatia, Nitik, et al.
Publicado: (2025)
por: Bhatia, Nitik, et al.
Publicado: (2025)
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
por: Kim, Dongjin, et al.
Publicado: (2025)
por: Kim, Dongjin, et al.
Publicado: (2025)
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
por: Holzenkamp, Matthias, et al.
Publicado: (2024)
por: Holzenkamp, Matthias, et al.
Publicado: (2024)
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
por: Kim, Dongjin, et al.
Publicado: (2025)
por: Kim, Dongjin, et al.
Publicado: (2025)
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
por: Della Pia, Flaviano, et al.
Publicado: (2025)
por: Della Pia, Flaviano, et al.
Publicado: (2025)
Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
por: Messerly, Mitchell, et al.
Publicado: (2025)
por: Messerly, Mitchell, et al.
Publicado: (2025)
MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
por: Lastre, Manuel González, et al.
Publicado: (2026)
por: Lastre, Manuel González, et al.
Publicado: (2026)
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
por: Fellman, Aslak, et al.
Publicado: (2024)
por: Fellman, Aslak, et al.
Publicado: (2024)
Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
por: Zhou, Chun, et al.
Publicado: (2026)
por: Zhou, Chun, et al.
Publicado: (2026)
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
por: Kuner, Matthew C., et al.
Publicado: (2025)
por: Kuner, Matthew C., et al.
Publicado: (2025)
Latent space design of interatomic potentials
por: Atlas, Susan R.
Publicado: (2026)
por: Atlas, Susan R.
Publicado: (2026)
Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides
por: Kılıç, Çetin, et al.
Publicado: (2024)
por: Kılıç, Çetin, et al.
Publicado: (2024)
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
por: Klimanova, Olga, et al.
Publicado: (2024)
por: Klimanova, Olga, et al.
Publicado: (2024)
Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
por: Mukhamedov, Boburjon, et al.
Publicado: (2024)
por: Mukhamedov, Boburjon, et al.
Publicado: (2024)
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
por: Shuang, Fei, et al.
Publicado: (2025)
por: Shuang, Fei, et al.
Publicado: (2025)
AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials
por: Bidoggia, Davide, et al.
Publicado: (2025)
por: Bidoggia, Davide, et al.
Publicado: (2025)
A KIM-compliant potfit for fitting sloppy interatomic potentials: Application to the EDIP model for silicon
por: Wen, Mingjian, et al.
Publicado: (2016)
por: Wen, Mingjian, et al.
Publicado: (2016)
FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
por: Kang, Hanwen, et al.
Publicado: (2025)
por: Kang, Hanwen, et al.
Publicado: (2025)
Self-consistent Coulomb interactions for machine learning interatomic potentials
por: Thomas, Jack, et al.
Publicado: (2024)
por: Thomas, Jack, et al.
Publicado: (2024)
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
por: Gumber, Shriya, et al.
Publicado: (2025)
por: Gumber, Shriya, et al.
Publicado: (2025)
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
por: Baghishov, Ilgar, et al.
Publicado: (2025)
por: Baghishov, Ilgar, et al.
Publicado: (2025)
Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
por: Lee, Sung-Ho, et al.
Publicado: (2023)
por: Lee, Sung-Ho, et al.
Publicado: (2023)
Knowing when to trust machine-learned interatomic potentials
por: Mehdi, Shams, et al.
Publicado: (2026)
por: Mehdi, Shams, et al.
Publicado: (2026)
Pushing the limits of unconstrained machine-learned interatomic potentials
por: Bigi, Filippo, et al.
Publicado: (2026)
por: Bigi, Filippo, et al.
Publicado: (2026)
Ejemplares similares
-
Adaptive-precision potentials for large-scale atomistic simulations
por: Immel, David, et al.
Publicado: (2024) -
Nanoindentation simulations for copper and tungsten with adaptive-precision potentials
por: Immel, David, et al.
Publicado: (2025) -
Graph atomic cluster expansion for foundational machine learning interatomic potentials
por: Lysogorskiy, Yury, et al.
Publicado: (2025) -
Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
por: Bochkarev, Anton, et al.
Publicado: (2026) -
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
por: Wang, Weishi, et al.
Publicado: (2025)