Paper Citation Record · LEDGER

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.16802.

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

pith.paper-citation-record.v1
2310.16802 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:07:35.857106Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:06:26.660660Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 06aea5b9-f948-4c3a-9b48-3df4a9338827 · inbound

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures cites this paper.

MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:13:39.648589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9296c5fe-263b-4b45-92e4-c5912a76c29e · inbound

Implicit Delta Learning of High Fidelity Neural Network Potentials cites this paper.

Implicit Delta Learning of High Fidelity Neural Network Potentials From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T20:07:35.857106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6169a03-bd6d-48e9-b917-147259dd4b6a · inbound

From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases cites this paper.

From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T13:41:14.546783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3df2e43b-fcea-43f7-96aa-f6ab3c2a7f2c · inbound

Distillation of atomistic foundation models across architectures and chemical domains cites this paper.

Distillation of atomistic foundation models across architectures and chemical domains From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:21.804538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f023287-4096-4323-bf92-c376f5a0b579 · inbound

PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface cites this paper.

PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:32:51.938305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8095ea9e-1e1b-4cc6-8897-d51e3dcb086f · inbound

GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond cites this paper.

GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:06:26.663473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.