Cover Image

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Farris, Riccardo, Telari, Emanuele, Artrith, Nongnuch, Neyman, Konstantin, Bruix, Albert
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
by: Dominguez, Jon Eunan Quinlivan, et al.
Published: (2025)

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

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)

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

Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
by: Ibragimova, Rina, et al.
Published: (2024)

Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
by: Schwalbe-Koda, Daniel, et al.
Published: (2024)

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

A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning
by: Kahle, Leonid, et al.
Published: (2024)

Machine Learning Potential Powered Insights into the Mechanical Stability of Amorphous Li-Si Alloys
by: Wei, Zixiong, et al.
Published: (2024)

Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations
by: Boonpalit, Kajjana, et al.
Published: (2024)

Structural Chart of Copper-Silver Nanoalloys through machine learning
by: Settem, Manoj, et al.
Published: (2026)

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

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials
by: Mahmoud, Chiheb Ben, et al.
Published: (2025)

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
by: Kurniawan, Yonatan, et al.
Published: (2025)

Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
by: Muuga, Kadri, et al.
Published: (2026)

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

Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials
by: Kundu, Sohang, et al.
Published: (2025)

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

PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
by: Mazitov, Arslan, et al.
Published: (2025)

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

A practical guide to machine learning interatomic potentials -- Status and future
by: Jacobs, Ryan, et al.
Published: (2025)

Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water
by: Thomsen, Bo, et al.
Published: (2024)

Highly Antioxidative Lithium Salt Enables High-Voltage Ether Electrolyte for Lithium Metal Battery
by: He, Jian, et al.
Published: (2024)

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential
by: Chen, Po-Yen, et al.
Published: (2026)

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

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

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

Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
by: Liu, Junlan, et al.
Published: (2024)

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

Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
by: Fletcher, Vincent G., et al.
Published: (2025)

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)

Universal rapid machine learning models for predicting unconvoluted and convoluted X-ray Absorption Spectra
by: Zhan, Fei, et al.
Published: (2026)

Machine learned potential for defected single layer hexagonal boron nitride
by: Janisch, John, et al.
Published: (2025)

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

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

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

Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
by: Wang, Menghang, et al.
Published: (2025)

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