Paper Citation Record · LEDGER

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2402.04379.

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

pith.paper-citation-record.v1
2402.04379 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:59.058119Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:09:36.527966Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d58ceea4-03cb-4088-9c74-dd6d048bebc4 · inbound

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools cites this paper.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 118

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:59.058119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 206075c3-feef-4c16-bd60-77094d1870d5 · inbound

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials cites this paper.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:32:24.645223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a04d22ea-cfae-4305-9886-0a1960331070 · inbound

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design cites this paper.

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:30:49.239391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eaf1d301-4430-4a70-a237-a4e668107d6e · inbound

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling cites this paper.

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:05:10.029165Z

Source-reported events for the cited work

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Observation dd55dae9-a756-4a0f-beb7-d3aaca500ab9 · inbound

Teaching LLMs to Speak Spectroscopy cites this paper.

Teaching LLMs to Speak Spectroscopy Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T20:46:50.020482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93910225-29e5-4306-b647-d001d1b8067d · inbound

Teaching LLMs to Speak Spectroscopy cites this paper.

Teaching LLMs to Speak Spectroscopy Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T20:46:50.126950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3fbf31b-6f9f-4f60-abf1-e37086697c90 · inbound

AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors cites this paper.

AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T09:20:54.585149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7616b077-c92d-4542-9d93-01e398746cf9 · inbound

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation cites this paper.

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-03T21:42:03.103083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a7cfeb3-ba6b-439b-8121-9670afa11c46 · inbound

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org cites this paper.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T16:56:20.292011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c70aaa3e-235f-44fc-90b4-e09b9055e1e7 · inbound

Inverse Design of Inorganic Compounds with Generative AI cites this paper.

Inverse Design of Inorganic Compounds with Generative AI Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 183

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Source-reported events for the cited work

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Observation 8526749a-b648-43a0-aeca-7328755e5f8c · inbound

Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space cites this paper.

Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:27.910948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f6e807d-aa3b-4949-bf44-e3ad5eed150f · inbound

Generation of magnetic metal-organic frameworks cites this paper.

Generation of magnetic metal-organic frameworks Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:11:28.828770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6218d04d-25d4-47eb-86cc-c60883ba69fc · 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 Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 298

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:10.274802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c30698f1-fdd6-45dd-954c-a7b1af47f7df · inbound

Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction cites this paper.

Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:31.585028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e45ad552-8589-4745-8c89-9d53c71bc03e · inbound

CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation cites this paper.

CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:52:40.104677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ba7cb3b-0a67-4e0c-b8b0-8b2c49ba8f34 · inbound

Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement cites this paper.

Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:35:46.609787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d00c24d-5450-4693-81cc-d84d5c413dd4 · inbound

Composable Crystals: Controllable Materials Discovery via Concept Learning cites this paper.

Composable Crystals: Controllable Materials Discovery via Concept Learning Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:55:05.879383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe7b2354-9e4f-4b61-965a-f86e60a31ff8 · inbound

General-purpose LLMs as Constrained Crystal Composition Generators cites this paper.

General-purpose LLMs as Constrained Crystal Composition Generators Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:11.232800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cad46a09-9265-436f-936f-67f3d2e48084 · inbound

LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms cites this paper.

LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:49:24.163532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86f1aacb-f1f8-474d-8743-1b3d10037cb8 · inbound

Atomistic Language Models Understand and Generate Materials cites this paper.

Atomistic Language Models Understand and Generate Materials Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T06:09:36.529777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 50f8f026-7135-4481-bd88-065caa1cd946 · inbound

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 253

Resolution: unresolved; no resolver link, observed 2026-07-11T16:17:26.963678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.