Paper Citation Record · LEDGER

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2506.23008.

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

pith.paper-citation-record.v1
2506.23008 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:57:42.380459Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T17:53:59.323119Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

20 of 20 outbound references displayed

verified exact4
verified fuzzy2
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation d77ceb4b-5927-4abc-898c-b82d977e7ea7 · outbound

This paper cites The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:57:43.974691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b13a29ab-5743-4521-9f6c-1fe73ebf2580 · outbound

This paper cites $\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials $\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-06T21:57:40.724337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9879ca5-fc7b-45e2-a352-13435f550695 · outbound

This paper cites The open molecules 2025 (omol25) dataset, evaluations, and models.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials The open molecules 2025 (omol25) dataset, evaluations, and models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:40.892188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59eb80ee-7c44-443f-86f3-4a71afe0c047 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Adam: A Method for Stochastic Optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:41.109088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e47ef2c-13aa-45fb-9e74-a51b32e410eb · outbound

This paper cites GeoMFormer: A General Architecture for Geometric Molecular Representation Learning.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials GeoMFormer: A General Architecture for Geometric Molecular Representation Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:57:43.259116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fa6d9943-71a5-4a65-9724-0d096005da7f · outbound

This paper cites Strategies for Pre-training Graph Neural Networks.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Strategies for Pre-training Graph Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:41.509846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b09da69c-bd76-4b86-8b6b-0d18159fdf4c · outbound

This paper cites Sliced Denoising: A Physics-Informed Molecular Pre-Training Method.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Sliced Denoising: A Physics-Informed Molecular Pre-Training Method

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:57:42.730047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cb855dfb-16e8-481c-bd41-73033ec07956 · outbound

This paper cites Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:41.868049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27aa989c-9b2e-4f5f-a2cc-1dc915a3e21f · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:41.990637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ffce5ce-d193-4600-9f09-6a2aa1acbe26 · outbound

This paper cites Equivariant Diffusion for Molecule Generation in 3D.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Equivariant Diffusion for Molecule Generation in 3D

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:42.084359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 119d0bca-2e5a-4dee-a431-20246d3ae748 · outbound

This paper cites GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:42.258429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b3954d2-5123-40d8-b999-c31461ae50eb · outbound

This paper cites GraphDF: A Discrete Flow Model for Molecular Graph Generation.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials GraphDF: A Discrete Flow Model for Molecular Graph Generation

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T21:57:42.563712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8dc55431-4731-4619-8f0b-24507d765afb · outbound

This paper cites Gradual Optimization Learning for Conformational Energy Minimization.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Gradual Optimization Learning for Conformational Energy Minimization

Reference 2000

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:57:43.619019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59b9975b-0f10-405a-9bdd-ebc3187b06f1 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Fast Graph Representation Learning with PyTorch Geometric

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:40.982639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e7c9acc-46a6-45cb-a6b8-9c53ed4ef328 · outbound

This paper cites Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:57:42.935579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a3e0fd42-14cb-490d-b288-55b6612dee81 · outbound

This paper cites Pre-training via Denoising for Molecular Property Prediction.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Pre-training via Denoising for Molecular Property Prediction

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:41.626207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf4d4fdf-dd8e-46e6-ad55-c5337c34118b · outbound

This paper cites OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:40.414127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88142563-9b1c-49d6-955e-3f02f5f6491e · outbound

This paper cites Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:40.257682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19b2dc00-09e6-4188-b41e-487bfcdc5c6e · outbound

This paper cites Open catalyst 2020 (oc20) dataset and community challenges.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Open catalyst 2020 (oc20) dataset and community challenges

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:57:44.203461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfdc2249-0083-45ef-949e-6745f6145f05 · outbound

This paper cites Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T21:57:40.217718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 457c970a-1561-4fb3-99ef-f0de0e639014 · inbound

Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases cites this paper.

Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:58:46.487859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.