Paper Citation Record · LEDGER

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

As of 17 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 3 inbound Pith citation observations for arXiv:2501.05211.

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

pith.paper-citation-record.v1
2501.05211 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:17:21.162421Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:57:52.419384Z

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

12 of 12 outbound references displayed

verified exact1
verified fuzzy3
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation e9154e3f-fbbf-4f91-85f2-19447d0203b3 · outbound

This paper cites Manthiram, ACS Cent.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Manthiram, ACS Cent

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:21.333908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa7272ff-14d8-49d9-87b9-e47fa085068a · outbound

This paper cites an unresolved cited work.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:21.162421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3aeb86e-73fd-4026-9e66-bb3ea60ee269 · outbound

This paper cites an unresolved cited work.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:17:21.254858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7272c7e0-309c-4951-a95c-b86d149d3868 · outbound

This paper cites A predictive machine learning force field framework for liquid electrolyte development.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery A predictive machine learning force field framework for liquid electrolyte development

Reference 146

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:17:21.240973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e79666a2-2d7e-4f93-8dde-edd39feb8a2a · outbound

This paper cites an unresolved cited work.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Unresolved cited work

Reference 1968

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:17:21.320766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 16b9a2e8-5978-47e1-8cc3-406e161605a2 · outbound

This paper cites Xu, Chem.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Xu, Chem

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:21.307857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eacc2f48-bcf7-4c5f-bc31-6dc1b2189153 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 2017

Resolution: malformed identifier; no resolver link, observed 2026-08-10T21:17:21.158353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9db51e7b-a41e-41e0-bbe8-3005abf29d21 · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 2453

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:21.145388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b991b72-01c7-4fe6-be59-828dae32744c · outbound

This paper cites A foundation model for atomistic materials chemistry.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery A foundation model for atomistic materials chemistry

Reference 2991

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:21.140716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4376761e-8d55-48bf-a107-cabbd401b461 · outbound

This paper cites Masias, J.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Masias, J

Reference 3151

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:17:21.294390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1fae8fa1-f050-455b-9d8b-61ba21a10097 · outbound

This paper cites an unresolved cited work.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Unresolved cited work

Reference 3441

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:17:21.281818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f26eb386-c04a-4a7f-9d45-8bafb2e244cb · outbound

This paper cites an unresolved cited work.

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery Unresolved cited work

Reference 4074

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:17:21.268116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 8efce05c-4038-4a1e-a6e0-bde1986f5b8b · inbound

A foundation model for atomistic materials chemistry cites this paper.

A foundation model for atomistic materials chemistry Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Reference 274

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:16:16.437764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 404be5f6-9a6b-4b6c-a864-ed06d3853b3f · inbound

Coarse-grained graph architectures for all-atom force predictions cites this paper.

Coarse-grained graph architectures for all-atom force predictions Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:56:54.172564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 15e02999-54d6-4979-b92a-d41c062c95fb · inbound

Active learning and explicit electrostatics enable accurate modeling of electrolytes cites this paper.

Active learning and explicit electrostatics enable accurate modeling of electrolytes Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T11:57:52.419384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.