Paper Citation Record · LEDGER

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.22208.

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

pith.paper-citation-record.v1
2505.22208 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:16:17.920852Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation ef1570da-d493-47f7-a1c7-28bd7db6d1bf · outbound

This paper cites Equivariant message passing for the prediction of tensorial properties and molecular spectra.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Equivariant message passing for the prediction of tensorial properties and molecular spectra

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:24.189670Z

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Observation c817c7d9-f615-48b2-a1f1-33d1d7ce2d3b · outbound

This paper cites Chgnet as a pretrained uni- versal neural network potential for charge-informed atomistic modelling.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Chgnet as a pretrained uni- versal neural network potential for charge-informed atomistic modelling

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:24.033730Z

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Observation edfb8630-2fe5-4865-91ea-f5403593b507 · outbound

This paper cites Equiformerv2: Improved equivariant transformer for scaling to higher- degree representations.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Equiformerv2: Improved equivariant transformer for scaling to higher- degree representations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:23.862540Z

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Observation ae2ff79c-8b9d-4f7c-8d93-65fca173d645 · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models A universal graph deep learning interatomic potential for the periodic table

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:23.646959Z

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Observation 8c0e17cd-bf60-4813-a2f3-fcf5598cf9e2 · outbound

This paper cites Mat- tersim: A deep learning atomistic model across elements, temperatures and pressures, 2024.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Mat- tersim: A deep learning atomistic model across elements, temperatures and pressures, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:23.396421Z

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Observation 1ff8188b-9b00-450a-86da-586d7c12203d · outbound

This paper cites E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:23.129150Z

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Observation 09bdd163-cc00-465b-be5f-d418cd495c99 · outbound

This paper cites Teanet: Universal neural network interatomic potential inspired by iterative electronic relaxations.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Teanet: Universal neural network interatomic potential inspired by iterative electronic relaxations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:22.930311Z

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Observation 24c7f0d8-830d-4171-906f-dffd9cbdf6fb · outbound

This paper cites Schnet: A continuous-filter convolutional neural network for modeling quantum inter- actions.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Schnet: A continuous-filter convolutional neural network for modeling quantum inter- actions

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:22.668509Z

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Observation 8291a296-c9a7-4783-af8d-cd5306280375 · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34:28877–28888, 2021.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34:28877–28888, 2021

Reference 9

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Source-reported events for the cited work

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Observation 7315be74-534e-45d5-8fc1-e3cd680bc1ed · outbound

This paper cites The open catalyst challenge 2021: Compe- tition report.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models The open catalyst challenge 2021: Compe- tition report

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:22.498852Z

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Observation 73a939d3-3e10-47b4-96af-9eb836432385 · outbound

This paper cites Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C

Reference 11

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Observation cdc4543b-65af-4473-96f6-13607a7caa5d · outbound

This paper cites Towards universal neural network potential for mate- rial discovery applicable to arbitrary combination of 45 elements.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Towards universal neural network potential for mate- rial discovery applicable to arbitrary combination of 45 elements

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:22.323678Z

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Observation c329341d-34cd-4250-9030-e666e126b183 · outbound

This paper cites End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:22.133405Z

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Observation d1fc53b0-2db3-4c76-964d-60871b79418d · outbound

This paper cites Directional message passing for molecular graphs.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Directional message passing for molecular graphs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:21.852350Z

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

Observation 2bb4779c-fd3a-4a59-9716-e37f4e12d469 · outbound

This paper cites Gemnet: universal directional graph neural networks for molecules.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Gemnet: universal directional graph neural networks for molecules

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:21.630334Z

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Observation b635921e-226b-4b16-a9a6-2d8dabf4dd51 · outbound

This paper cites Lawrence Zitnick, and Abhishek Das.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Lawrence Zitnick, and Abhishek Das

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:21.436572Z

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Observation f2342d8a-49a1-49b2-9c30-81e0094bb88f · outbound

This paper cites Lawrence Zitnick, and Zachary Ulissi.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Lawrence Zitnick, and Zachary Ulissi

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:21.250026Z

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Observation 83351abc-08ec-487f-9f68-666107951c69 · outbound

This paper cites Wood, Sid- dharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Am- mar Rizvi, Nima Shoghi, Anuroop Sriram, F´ elix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Wood, Sid- dharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Am- mar Rizvi, Nima Shoghi, Anuroop Sriram, F´ elix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.987771Z

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Observation f47c9087-3e87-4a3b-8589-5b7a618daa8b · outbound

This paper cites Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.819313Z

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

Observation 12b38e50-fd6b-4749-977e-eff83c100f6a · outbound

This paper cites Elena, D´ avid P.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Elena, D´ avid P

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.619348Z

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

Observation c25bcd7d-f027-4c6e-bb4a-9d56db96a7e1 · outbound

This paper cites Mace: Higher order equivariant message passing neural networks 22 for fast and accurate force fields.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Mace: Higher order equivariant message passing neural networks 22 for fast and accurate force fields

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.473496Z

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

Observation 74b586c0-25dd-4a7a-90da-6e24710d5e2b · outbound

This paper cites York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, and Han Wang.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, and Han Wang

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.315464Z

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

Observation 14aaf6fe-e6c0-44c7-a0f7-4414619448e7 · outbound

This paper cites https://www.preferred.jp/en/news/p r20240917/.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models https://www.preferred.jp/en/news/p r20240917/

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.164320Z

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Observation f86d3d5d-9cd2-4822-9fe4-3ec4b3bafb16 · outbound

This paper cites Kitchin, Zachary Ward Ulissi, C.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Kitchin, Zachary Ward Ulissi, C

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:20.010201Z

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

Observation a0ea4535-d4f0-4c71-8a09-3757d23e635e · outbound

This paper cites Fractional denoising for 3d molecular pre-training.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Fractional denoising for 3d molecular pre-training

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:19.851658Z

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

Observation 0f0b161d-d00f-453d-ba20-f12cea2a9caa · outbound

This paper cites Pub- chem3d: a new resource for scientists.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Pub- chem3d: a new resource for scientists

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:19.628651Z

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Observation 411af461-377f-4cb2-ad77-64c89805b81d · outbound

This paper cites Transition1x-a dataset for building generalizable reactive machine learning potentials.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Transition1x-a dataset for building generalizable reactive machine learning potentials

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:19.420621Z

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Observation 99281390-2895-4c1f-bf36-937fbdd186a9 · outbound

This paper cites Qm7-x, a comprehensive dataset of quantum-mechanical properties span- ning the chemical space of small organic molecules.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Qm7-x, a comprehensive dataset of quantum-mechanical properties span- ning the chemical space of small organic molecules

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:19.207453Z

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Observation bc67ae2a-00df-45c1-b60f-8cd1e43a9954 · outbound

This paper cites Qmugs, quantum mechanical properties of drug-like molecules.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Qmugs, quantum mechanical properties of drug-like molecules

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:18.997444Z

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Observation 00d2542a-4e2c-4dfa-98fc-31e4e64029fe · outbound

This paper cites https://github.com/FAIR-Chem/fairchem/p ull/267.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models https://github.com/FAIR-Chem/fairchem/p ull/267

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:18.830784Z

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Observation 40875b54-45fb-42cd-bec8-c0627a7a2f99 · outbound

This paper cites Abci 2.0: Advances in open ai computing infrastructure at aist.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Abci 2.0: Advances in open ai computing infrastructure at aist

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:18.572344Z

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Observation e2809323-5287-45ea-9a08-f32f065180d9 · outbound

This paper cites Umap: Uniform manifold approximation and projection.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models Umap: Uniform manifold approximation and projection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:18.346289Z

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Observation 191fb353-4ce3-4383-b3a8-764b2cf7a6bb · outbound

This paper cites http: //www.jmol.org/.

LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models http: //www.jmol.org/

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:16:18.106665Z

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

Pith citing papers

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