Paper Citation Record · LEDGER

On-the-fly machine learning force field generation: Application to melting points

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1904.12961.

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

pith.paper-citation-record.v1
1904.12961 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T10:39:56.302357Z

measured 1 of 1 external citation measurements

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

Source: doi_reference, observed 2026-06-28T10:42:00.029726Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

577
doi_reference, observed 2026-06-28T10:42:00.029726Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 827e0fc7-ba7f-4574-a4f1-0a09c5b90ba5 · inbound

A Unified microscopic picture of cation and anion migration in MAPbI$_3$ cites this paper.

A Unified microscopic picture of cation and anion migration in MAPbI$_3$ On-the-fly machine learning force field generation: Application to melting points

Reference 6

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 a10f20f0-f29b-4799-90e6-d3075c50ee5c · inbound

Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials cites this paper.

Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials On-the-fly machine learning force field generation: Application to melting points

Reference 4

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 5659c51e-5e82-4413-97b6-e995d765a73f · inbound

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs cites this paper.

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs On-the-fly machine learning force field generation: Application to melting points

Reference 21

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 a9bfb9d7-5854-4e2d-b53a-06a727430cec · inbound

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs cites this paper.

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs On-the-fly machine learning force field generation: Application to melting points

Reference 21

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 ffb3b14a-c2d0-4097-acff-f974f13b6247 · inbound

Effective dynamic constants for nonequilibrium third-principles simulations cites this paper.

Effective dynamic constants for nonequilibrium third-principles simulations On-the-fly machine learning force field generation: Application to melting points

Reference 3

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 21569300-4513-4c87-9ef3-10c269ccfd36 · inbound

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs cites this paper.

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs On-the-fly machine learning force field generation: Application to melting points

Reference 24

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 932b79d3-5f36-4817-a66c-3cffc2f9eb58 · inbound

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs cites this paper.

Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs On-the-fly machine learning force field generation: Application to melting points

Reference 23

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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 18e9595f-e32f-4f29-9c89-f9386be5b3ae · inbound

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials cites this paper.

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials On-the-fly machine learning force field generation: Application to melting points

Reference 5

Resolution: verified exact; doi, observed 2026-07-04T23:49:06.528960Z

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.