Cover Image

Conservative adaptive-precision interatomic potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Immel, David, Drautz, Ralf, Sutmann, Godehard
Format:	Preprint
Published:	2025
Subjects:	Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive-precision potentials for large-scale atomistic simulations
by: Immel, David, et al.
Published: (2024)

Nanoindentation simulations for copper and tungsten with adaptive-precision potentials
by: Immel, David, et al.
Published: (2025)

Graph atomic cluster expansion for foundational machine learning interatomic potentials
by: Lysogorskiy, Yury, et al.
Published: (2025)

Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
by: Bochkarev, Anton, et al.
Published: (2026)

Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
by: Wang, Weishi, et al.
Published: (2025)

Repulsive interatomic potentials calculated at three levels of theory
by: Nordlund, Kai, et al.
Published: (2025)

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

High-performance training and inference for deep equivariant interatomic potentials
by: Tan, Chuin Wei, et al.
Published: (2025)

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

Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)

Machine-learning interatomic potential for AlN for epitaxial simulation
by: Taormina, Nicholas, et al.
Published: (2025)

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

Accelerating point defect photo-emission calculations with machine learning interatomic potentials
by: Sharma, Kartikeya, et al.
Published: (2025)

Suitability of available interatomic potentials for Sn to model its 2D allotropes
by: Maździarz, Marcin
Published: (2024)

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

Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction
by: Bhatia, Nitik, et al.
Published: (2025)

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

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
by: Holzenkamp, Matthias, et al.
Published: (2024)

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)

Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
by: Della Pia, Flaviano, et al.
Published: (2025)

Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
by: Messerly, Mitchell, et al.
Published: (2025)

MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
by: Lastre, Manuel González, et al.
Published: (2026)

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

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

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

Latent space design of interatomic potentials
by: Atlas, Susan R.
Published: (2026)

Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides
by: Kılıç, Çetin, et al.
Published: (2024)

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

Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
by: Mukhamedov, Boburjon, et al.
Published: (2024)

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
by: Shuang, Fei, et al.
Published: (2025)

AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials
by: Bidoggia, Davide, et al.
Published: (2025)

A KIM-compliant potfit for fitting sloppy interatomic potentials: Application to the EDIP model for silicon
by: Wen, Mingjian, et al.
Published: (2016)

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

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

Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
by: Gumber, Shriya, 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)

Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
by: Lee, Sung-Ho, et al.
Published: (2023)

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

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