Paper Citation Record · LEDGER

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2010.09435.

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

pith.paper-citation-record.v1
2010.09435 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:53:29.096892Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T18:58:19.969095Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 30aa7da5-0f19-47a7-a86c-4b60dc70febc · inbound

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:42:26.265905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation de47c201-5826-4bb7-9185-94fcdaf5401a · inbound

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models cites this paper.

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:58:19.973194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 599ff6c5-980c-418f-9fa2-092d67672e6a · inbound

Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models cites this paper.

Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T18:15:29.901837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f4e7b3-fbe0-48de-85f6-ebd550f1a674 · inbound

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials cites this paper.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 137

Resolution: unresolved; no resolver link, observed 2026-08-09T04:14:42.626761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ca37625-89b7-42d2-8802-8ec7adfcf2f1 · inbound

LAMBench: A Benchmark for Large Atomistic Models cites this paper.

LAMBench: A Benchmark for Large Atomistic Models An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T05:53:29.096892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40c6668b-eb48-474f-8d5d-0138e661b463 · inbound

Understanding Learning Invariance in Deep Linear Networks cites this paper.

Understanding Learning Invariance in Deep Linear Networks An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:54.768319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 717c9466-9014-45d1-aa75-8c519fe59147 · inbound

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry cites this paper.

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

Reference 182

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:10.384144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.