Paper Citation Record · LEDGER

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles

As of 7 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2607.03433.

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

pith.paper-citation-record.v1
2607.03433 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

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measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:08:16.536665Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

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Outbound references

Observation cf34d767-14ed-44e7-8981-911af683ba91 · outbound

This paper cites an unresolved cited work.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

Reference 1

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Observation d1ddd959-ee73-4581-9cfa-fe8741b4dd35 · outbound

This paper cites an unresolved cited work.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

Reference 2

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Observation 36d4829b-98a6-427f-80c8-b7275a898414 · outbound

This paper cites Jacobs , author D.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Jacobs , author D

Reference 3

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This paper cites an unresolved cited work.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

Reference 4

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Observation bc0344f7-ae97-44e9-96a0-a40f7145951a · outbound

This paper cites Six Open Questions in Machine-Learned Interatomic Potential Foundation Models.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Six Open Questions in Machine-Learned Interatomic Potential Foundation Models

Reference 5

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This paper cites Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies

Reference 6

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Observation 47a0d27e-9c07-4fca-8687-08d610a8386b · outbound

This paper cites Fast and Fourier Features for Transfer Learning of Interatomic Potentials.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Fast and Fourier Features for Transfer Learning of Interatomic Potentials

Reference 7

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Observation c78ec005-71a5-431e-a992-50b7774c72c2 · outbound

This paper cites Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 8

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Observation 79dc4ac0-eb5b-43bd-8e6a-2122220330c0 · outbound

This paper cites Hänseroth , author A.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Hänseroth , author A

Reference 9

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Observation 30c0099d-7150-4b8c-ad6a-234f2c7f5685 · outbound

This paper cites Fine-tuning MLIP foundation models: strategies for accuracy and transferability.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Fine-tuning MLIP foundation models: strategies for accuracy and transferability

Reference 10

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Observation d49986e1-63f6-4513-afae-4f86b82fcf1c · outbound

This paper cites Merchant , author S.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Merchant , author S

Reference 11

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Observation 9f2cea6f-df1a-4708-ae1a-8efd6af42937 · outbound

This paper cites Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning

Reference 12

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Observation 43e8d9d8-b5a6-45e7-844a-51dabd5d6d23 · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles System of Agentic AI for the Discovery of Metal-Organic Frameworks

Reference 13

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Observation ca1fd881-4faa-4d21-85e3-cedbe63f41e7 · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Riebesell , author R

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T02:31:03.871783Z

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Observation e04c60af-8a1e-4ab4-8c1a-e851106fdb23 · outbound

This paper cites Chiang , author T.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Chiang , author T

Reference 15

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Observation 57745e04-17a6-4214-80ad-78e9fcbd553a · outbound

This paper cites Choudhary , author D.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Choudhary , author D

Reference 16

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Observation ea018e90-9bbd-40aa-ba6b-ead6705e7dfa · outbound

This paper cites Peng , author C.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Peng , author C

Reference 17

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Observation 3c0fce38-0897-44ab-b797-dce212fb41ec · outbound

This paper cites Thermal Conductivity Predictions with Foundation Atomistic Models.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T02:31:03.871783Z

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Observation a3c86de3-91f1-4039-a5a1-7e7b782a347d · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Moon , author U

Reference 19

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Observation 24eb77c1-7645-48e4-94ae-00b2f059af01 · outbound

This paper cites Loew , author J.

Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Loew , author J

Reference 20

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Kraß , author J

Reference 21

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Observation a411abc9-858a-4759-b038-87965a114813 · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Focassio , author L

Reference 22

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Deng , author Y

Reference 23

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Wines \ and\ author K

Reference 24

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Deng , author P

Reference 25

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Schmidt , author T

Reference 26

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 27

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 28

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Malosso , author F

Reference 29

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Observation 41bfbb40-ddda-4696-a328-ea4c2802ae60 · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Pushing the limits of unconstrained machine-learned interatomic potentials

Reference 32

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Blum , author R

Reference 34

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Litman , author V

Reference 35

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Hjorth Larsen , author J

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Batatia , author P

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Bochkarev , author Y

Reference 40

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Orb: A Fast, Scalable Neural Network Potential

Reference 41

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Lysogorskiy , author A

Reference 42

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Observation 1d85575e-b4cd-48ec-9cd1-aafe687b7a28 · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Orb-v3: atomistic simulation at scale

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Observation a175c471-bd63-42dc-a66f-9c648c8e5da6 · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 44

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles High-performance training and inference for deep equivariant interatomic potentials

Reference 45

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Batatia , author C

Reference 47

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Observation f25892ae-8e05-482b-b69e-0907c7a0482f · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Tuckerman ,\ @noop en title Statistical Mechanics : Theory and Molecular Simulation \ ( publisher OUP Oxford ,\ year 2010 )\ note google-Books-ID: Lo3Jqc0pgrcC NoStop

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Kubo ,\ title title Statistical- Mechanical Theory of Irreversible Processes

Reference 50

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Observation cfab0bdb-628a-4cef-8a80-83e53275fd2c · outbound

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Grimme , author A

Reference 53

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles o rkman , author P. Blaha , author S. Bl \

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Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles Unresolved cited work

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Pith citing papers

Observation c3097bfc-8f41-4a7e-a701-67deee5304a8 · inbound

Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials cites this paper.

Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles

Reference 3

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