Paper Citation Record · LEDGER

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations

As of 17 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2607.06969.

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

pith.paper-citation-record.v1
2607.06969 v2

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:13:51.837327Z

measured 6 of 6 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

6 of 6 outbound references displayed

verified exact0
verified fuzzy0
unresolved4
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a674bb02-5791-4ef4-aef1-7eba95533c92 · outbound

This paper cites The reliability and efficiency of MD simulations are largely determined by the interatomic potential.

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations The reliability and efficiency of MD simulations are largely determined by the interatomic potential

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T08:13:51.194847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc1f1ba0-8b10-47b9-8b7b-cd6931aa6c61 · outbound

This paper cites an unresolved cited work.

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T08:13:51.345009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a96001b1-9351-4c77-b2cb-71c009bde179 · outbound

This paper cites Architecture As illustrated in Figure 1, AI2Pot adopts a layered architecture that separates high-level MLIP models development from performance-critical atomistic computation.

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations Architecture As illustrated in Figure 1, AI2Pot adopts a layered architecture that separates high-level MLIP models development from performance-critical atomistic computation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T08:13:51.501431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0c5994b-0e6a-4762-948f-b6cccfaef627 · outbound

This paper cites Potential training for Ge-Sb-Te ternary system This section demonstrates the training capability of AI2Pot using a Ge-Sb-Te dataset as a representative example.

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations Potential training for Ge-Sb-Te ternary system This section demonstrates the training capability of AI2Pot using a Ge-Sb-Te dataset as a representative example

Reference 4

Resolution: malformed identifier; no resolver link, observed 2026-08-02T08:13:51.654753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4f0b3fb-65e7-4368-b657-e76862c23e79 · outbound

This paper cites Figure 8.EffectofbatchsizeonMTPaccuracyandtrainingefficiency.

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations Figure 8.EffectofbatchsizeonMTPaccuracyandtrainingefficiency

Reference 5

Resolution: malformed identifier; no resolver link, observed 2026-08-02T08:13:51.757168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea95bec6-e153-41c1-bbc8-350737d7fe9b · outbound

This paper cites an unresolved cited work.

AI2Pot: A scalable and unified framework for machine-learning interatomic potential development and large-scale molecular dynamic simulations Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T08:13:51.837327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.