Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2509.19180.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.19180 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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Pith citing papers itemized under the disclosed page cap.

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Source: arxiv_reference, observed 2026-05-11T15:16:10.873607Z

Reference resolution

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External citation measurements

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Outbound references

Observation 9c2670ab-d0b2-433e-acbf-eec671fe04d4 · outbound

This paper cites Behler and M.

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

Reference 1

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This paper cites an unresolved cited work.

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

Reference 2

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Observation 03b6f98d-c9ce-4736-9898-6f50e88f84fa · outbound

This paper cites A foundation model for atomistic materials chemistry.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials A foundation model for atomistic materials chemistry

Reference 3

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Observation 970201bb-8113-4d6c-8bb9-f1342eb0555d · outbound

This paper cites Deringer, Miguel A.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Deringer, Miguel A

Reference 4

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Observation 8b471380-c1b9-4551-ade5-5c128252ce8b · outbound

This paper cites Perspective: Machine learning potentials for atomistic simulations.The Journal of chemical physics, 145(17), 2016.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Perspective: Machine learning potentials for atomistic simulations.The Journal of chemical physics, 145(17), 2016

Reference 5

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Observation b518d363-dc50-45a8-9faf-4e20380e8ebb · outbound

This paper cites Performance and cost assessment of machine learning interatomic potentials.The Journal of Physical Chemistry A, 124(4):731–745, 2020.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Performance and cost assessment of machine learning interatomic potentials.The Journal of Physical Chemistry A, 124(4):731–745, 2020

Reference 6

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Observation 45c7e8cf-efb2-4090-acdc-b4059879d401 · outbound

This paper cites Machine learning for interatomic potential models.

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

Reference 7

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This paper cites Artrith et al.

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

Reference 8

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Observation 796178f0-b378-41ab-bb9d-ea690fa9904b · outbound

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

Reference 9

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This paper cites Ulissi, and Andrew J.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Ulissi, and Andrew J

Reference 10

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Observation e57891c6-ce37-440d-ac9e-92b03a7a4a02 · outbound

This paper cites Schmidt, Ole Winther, Tejs Vegge, and Peter Bjørn Jørgensen.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Schmidt, Ole Winther, Tejs Vegge, and Peter Bjørn Jørgensen

Reference 11

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Observation 77eb0f9d-4ab0-4f8f-b108-42036e18ae96 · outbound

This paper cites Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Statistical methods for resolving poor uncertainty quantification in machine learning interatomic potentials

Reference 12

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Observation f49def94-2cca-4642-8578-1316c122be48 · outbound

This paper cites Uncertainty-driven dynamics for active learning of interatomic potentials.Nature computational science, 3(3):230–239, 2023.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Uncertainty-driven dynamics for active learning of interatomic potentials.Nature computational science, 3(3):230–239, 2023

Reference 13

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Observation a42ddea6-b72c-4ab0-96a7-5cda0fef87a0 · outbound

This paper cites Efficient ensemble uncertainty estimation in gaussian processes regression.Machine Learning: Science and Technology, 5(4):045029, 2024.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Efficient ensemble uncertainty estimation in gaussian processes regression.Machine Learning: Science and Technology, 5(4):045029, 2024

Reference 14

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Observation 6506e2ef-3f15-4088-9af0-bf8c6c0b7449 · outbound

This paper cites Uncertainty quantification by direct propagation of shallow ensembles.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Uncertainty quantification by direct propagation of shallow ensembles

Reference 15

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Observation beb16c99-3c40-439b-8ac4-a79927732b3c · outbound

This paper cites Uncertainty quantification in molecular simulations with dropout neural network potentials.npj computational materials, 6(1):124, 2020.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Uncertainty quantification in molecular simulations with dropout neural network potentials.npj computational materials, 6(1):124, 2020

Reference 16

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Observation 3a27afa3-d97b-46fc-9103-9f1b753407f8 · outbound

This paper cites Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials.Machine Learning: Science and Technology, 3(4):045028, 2022.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials.Machine Learning: Science and Technology, 3(4):045028, 2022

Reference 17

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Observation dc6368f5-d503-4cc2-b182-e62fd013773d · outbound

This paper cites Fast uncertainty estimates in deep learning interatomic potentials.The Journal of Chemical Physics, 158(16), 2023.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Fast uncertainty estimates in deep learning interatomic potentials.The Journal of Chemical Physics, 158(16), 2023

Reference 18

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Observation f64a2391-6bdb-45e9-974b-c67f782aecae · outbound

This paper cites Uncertainty quantification for neural network potential foundation models.npj Computational Materials, 11(1):109, 2025.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Uncertainty quantification for neural network potential foundation models.npj Computational Materials, 11(1):109, 2025

Reference 19

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Observation 9134053c-1471-4058-a3ba-afdcd9a3f3c3 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.Advances in neural information processing systems, 30, 2017.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Simple and scalable predictive uncertainty estimation using deep ensembles.Advances in neural information processing systems, 30, 2017

Reference 20

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This paper cites Grambow, Barbara Pernici, Yi Pei Li, and William H.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Grambow, Barbara Pernici, Yi Pei Li, and William H

Reference 21

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Observation 9d96141a-970d-4527-ab4a-05fe087ceed1 · outbound

This paper cites Fast uncertainty estimates in deep learning interatomic potentials.Journal of Chemical Physics, 158, 4 2023.

Reference 22

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Observation bc032638-6a3e-413a-9514-fbb17298ccac · outbound

This paper cites Single- model uncertainty quantification in neural network potentials does not consistently outperform model ensembles.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Single- model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

Reference 23

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Observation 38d4c0a1-44d3-4853-89b7-eafcced4322e · outbound

This paper cites Providing Machine Learning Potentials with High Quality Uncertainty Estimates.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Providing Machine Learning Potentials with High Quality Uncertainty Estimates

Reference 24

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This paper cites Quality of uncertainty estimates from neural network potential ensembles.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Quality of uncertainty estimates from neural network potential ensembles

Reference 25

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

Reference 26

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This paper cites On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials

Reference 27

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

Reference 28

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This paper cites Hands-on bayesian neural networks—a tutorial for deep learning users.IEEE Computational Intelligence Magazine, 17(2): 29–48, 2022.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Hands-on bayesian neural networks—a tutorial for deep learning users.IEEE Computational Intelligence Magazine, 17(2): 29–48, 2022

Reference 29

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This paper cites Chapman and Hall/CRC, 1st edition, 2011.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Chapman and Hall/CRC, 1st edition, 2011

Reference 30

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

Reference 31

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This paper cites An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for tio2.Computational Materials Science, 114:135–150, 3 2016.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for tio2.Computational Materials Science, 114:135–150, 3 2016

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This paper cites Aretxabaleta, In Won Yeu, Iñigo Etxebarria, Hegoi Manzano, and Nongnuch Artrith.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Aretxabaleta, In Won Yeu, Iñigo Etxebarria, Hegoi Manzano, and Nongnuch Artrith

Reference 33

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Observation 239c1dd3-e88b-4ba5-ac7e-58176aa018f8 · outbound

This paper cites A benchmark on uncertainty quantifica- tion for deep learning prognostics.Reliability Engineering & System Safety, 253:110513, 2025.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials A benchmark on uncertainty quantifica- tion for deep learning prognostics.Reliability Engineering & System Safety, 253:110513, 2025

Reference 34

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This paper cites Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D

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This paper cites Tyxe: Pyro-based bayesian neural nets for pytorch.Proceedings of Machine Learning and Systems, 4:398–413, 2022.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Tyxe: Pyro-based bayesian neural nets for pytorch.Proceedings of Machine Learning and Systems, 4:398–413, 2022

Reference 36

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This paper cites Deep ensembles secretly perform empirical bayes,.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Deep ensembles secretly perform empirical bayes,

Reference 37

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Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Blei, Alp Kucukelbir, and Jon D

Reference 38

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Observation c1bddfba-6da5-49a5-aeee-5ad4bfb39da6 · outbound

This paper cites On information and sufficiency.The annals of mathematical statistics, 22(1):79–86, 1951.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials On information and sufficiency.The annals of mathematical statistics, 22(1):79–86, 1951

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Observation 901dc7d6-1940-4365-97b3-a063686c1bcf · outbound

This paper cites Stochastic variational inference.the Journal of machine Learning research, 14(1):1303–1347, 2013.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Stochastic variational inference.the Journal of machine Learning research, 14(1):1303–1347, 2013

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Observation f7d24202-5887-48bf-97de-9cc8831d35d3 · outbound

This paper cites Operations for learning with graphical models.Journal of artificial intelligence research, 2: 159–225, 1994.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Operations for learning with graphical models.Journal of artificial intelligence research, 2: 159–225, 1994

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Observation b4b44ab9-3d71-4879-9e18-99dd6ced2ede · outbound

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Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Auto-Encoding Variational Bayes

Reference 42

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Observation c2c6d562-4c10-43c5-b471-72e1c5e0f3fb · outbound

This paper cites Variational dropout and the local reparameterization trick.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Variational dropout and the local reparameterization trick

Reference 43

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Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

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Observation 7ec54722-646a-4173-8906-2abb332576ec · outbound

This paper cites Radial bayesian neural networks: Beyond discrete support in large-scale bayesian deep learning.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Radial bayesian neural networks: Beyond discrete support in large-scale bayesian deep learning

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Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials PyTorch Lightning, March 2019

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Reference 47

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This paper cites Accurate uncertainties for deep learning using calibrated regression.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Accurate uncertainties for deep learning using calibrated regression

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Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Optuna: A next-generation hyperparameter optimization framework

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This paper cites Deep Ensembles Secretly Perform Empirical Bayes.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Deep Ensembles Secretly Perform Empirical Bayes

Reference 2025

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This paper cites URL https://www.sciencedirect.com/science/ article/pii/S0951832024005854.

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials URL https://www.sciencedirect.com/science/ article/pii/S0951832024005854

Reference 8320

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Pith citing papers

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Knowing when to trust machine-learned interatomic potentials cites this paper.

Knowing when to trust machine-learned interatomic potentials Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials

Reference 38

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Observation 4e19e9a7-767f-4d22-8bf7-65b5c942ae60 · inbound

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials

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