Cover Image

Efficient ensemble uncertainty estimation in Gaussian Processes Regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Christiansen, Mads-Peter Verner, Rønne, Nikolaj, Hammer, Bjørk
Format:	Preprint
Published:	2024
Subjects:	Computational Physics Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Atomistic Generative Diffusion for Materials Modeling
by: Rønne, Nikolaj, et al.
Published: (2025)

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

Generative diffusion model for surface structure discovery
by: Rønne, Nikolaj, et al.
Published: (2024)

Generating candidates in global optimization algorithms using complementary energy landscapes
by: Slavensky, Andreas Møller, et al.
Published: (2024)

Accelerating structure search using atomistic graph-based classifiers
by: Slavensky, Andreas Møller, et al.
Published: (2024)

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

A bivariational, stable and convergent hierarchy for time-dependent coupled cluster with adaptive basis sets
by: Højlund, Mads Greisen, et al.
Published: (2024)

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions
by: Goswami, Rohit, et al.
Published: (2025)

Shoot from the HIP: Hessian Interatomic Potentials without derivatives
by: Burger, Andreas, et al.
Published: (2025)

Kernel Ridge Regression for conformer ensembles made easy with Structured Orthogonal Random Features
by: Karandashev, Konstantin
Published: (2025)

Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
by: Christiansen, Mads-Peter Verner, et al.
Published: (2025)

$Δ$-model correction of Foundation Model based on the models own understanding
by: Christiansen, Mads-Peter Verner, et al.
Published: (2025)

Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials
by: Krondorfer, Johannes K., et al.
Published: (2025)

Accurate Helium-Benzene Potential: from CCSD(T) to Gaussian Process Regression
by: Akram, Shahzad, et al.
Published: (2026)

Comprehensive reevaluation of acetaldehyde chemistry and the underlying uncertainties
by: Ren, Xinrui, et al.
Published: (2024)

Fault-tolerant quantum computations of vibrational wave functions
by: Majland, Marco, et al.
Published: (2025)

Accelerating Instanton Theory with the Line Integral String Method, Gaussian Process Regression, and Selective Hessian Modeling
by: Zhang, Chenghao, et al.
Published: (2026)

Disorder-induced non-Gaussian states in large ensembles of cavity-coupled molecules
by: Schwengelbeck, R., et al.
Published: (2026)

η-ensemble path integral Monte Carlo approach to the free energy of the warm dense electron gas and the uniform electron liquid
by: Dornheim, Tobias, et al.
Published: (2024)

Memory-Efficient Recursive Evaluation of 3-Center Gaussian Integrals
by: Asadchev, Andrey, et al.
Published: (2022)

A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
by: Goswami, Rohit
Published: (2026)

Boosting ensemble refinement with transferable force field corrections: synergistic optimization for molecular simulations
by: Gilardoni, Ivan, et al.
Published: (2023)

Anharmonic infrared spectra of cationic pyrene and superhydrogenated derivatives
by: Tang, Zeyuan, et al.
Published: (2025)

Active Δ-learning with universal potentials for global structure optimization
by: Pitfield, Joe, et al.
Published: (2025)

Cascading symmetry constraint during machine learning-enabled structural search for sulfur induced Cu(111)-$(\sqrt{43}\times\sqrt{43})$ surface reconstruction
by: Brix, Florian, et al.
Published: (2025)

WeTICA: A directed search weighted ensemble based enhanced sampling method to estimate rare event kinetics in a reduced dimensional space
by: Mitra, Sudipta, et al.
Published: (2025)

Augmentation of Universal Potentials for Broad Applications
by: Pitfield, Joe, et al.
Published: (2024)

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
by: Farris, Riccardo, et al.
Published: (2025)

Translational eigenstates of He@C$_{60}$ from four-dimensional \textit{ab initio} Potential Energy Surfaces interpolated using Gaussian Process Regression
by: Panchagnula, K., et al.
Published: (2024)

Benchmarking of quantum fidelity kernels for Gaussian process regression
by: Guo, Xuyang, et al.
Published: (2024)

Neural network ensemble for computing cross sections for rotational transitions in H$_{2}$O + H$_{2}$O collisions
by: Mandal, Bikramaditya, et al.
Published: (2025)

Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications
by: Wang, Yuanheng, et al.
Published: (2024)

Time-dependent Gaussian basis sets for many-body systems using Rothe's method: A mean-field study
by: Schrader, Simon Elias, et al.
Published: (2025)

Single-Hessian thawed Gaussian approximation: The missing rung on the ladder
by: Begušić, Tomislav, et al.
Published: (2019)

A complex Gaussian representation of continuum wavefunctions respectful of their asymptotic behaviour
by: Nana, Stéphanie Laure Egome, et al.
Published: (2025)

Deep learning parameter estimation and quantum control of single molecule
by: Scarpetta, Juan M., et al.
Published: (2026)

Efficient ground-state energy estimation and certification on early fault-tolerant quantum computers
by: Wang, Guoming, et al.
Published: (2023)

Stable, Fast, and Accurate Kohn-Sham Inversion in Gaussian Basis for Open Shell Molecular and Condensed Phase Systems via Density Matrix Penalization
by: Chai, Ziwei, et al.
Published: (2026)

PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units
by: Athavale, Vishikh, et al.
Published: (2025)

Quantum Gaussian process model of potential energy surface for a polyatomic molecule
by: Dai, Jun, et al.
Published: (2022)