Paper Citation Record · LEDGER

Thermal Conductivity Predictions with Foundation Atomistic Models

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2408.00755.

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

pith.paper-citation-record.v1
2408.00755 v5

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measured 19 of 19 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:06:04.868298Z

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Source: arxiv_reference, observed 2026-07-03T06:57:42.910912Z

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Pith citing papers

Observation f4f3086b-15f7-4d98-bd08-2e0c8b0ea488 · inbound

Heat transport in crystalline organic semiconductors: coexistence of phonon propagation and tunneling cites this paper.

Heat transport in crystalline organic semiconductors: coexistence of phonon propagation and tunneling Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 60

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Observation 5eec165a-b7f9-44d1-99d8-06bb920cbb62 · inbound

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties cites this paper.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.858735Z

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Observation 205f5ff5-811a-4ba3-b075-bb406b883125 · inbound

Universal Machine Learning Interatomic Potentials are Ready for Phonons cites this paper.

Universal Machine Learning Interatomic Potentials are Ready for Phonons Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 46

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Observation 59383cf3-a85e-4148-91eb-2eecb3877df8 · inbound

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

Fast and Fourier Features for Transfer Learning of Interatomic Potentials Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:06:04.868298Z

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Observation 3ca4cd0d-bf13-42c0-a767-6c54bc536f8a · inbound

Higher-order thermal transport theory for phonon thermal transport in semiconductors using lattice dynamics calculations and the Boltzmann transport equation cites this paper.

Higher-order thermal transport theory for phonon thermal transport in semiconductors using lattice dynamics calculations and the Boltzmann transport equation Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 126

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Observation 0c77d3e0-7b02-4e87-b9cf-418b93f569e4 · inbound

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations cites this paper.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T10:55:21.583627Z

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Observation 0437decb-f736-4089-a8c6-05e44a607fe0 · inbound

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

Distillation of atomistic foundation models across architectures and chemical domains Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:21.838922Z

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Observation 0580ab74-b464-4510-869e-9bdf464d8809 · inbound

High-throughput computational framework for lattice dynamics and thermal transport including high-order anharmonicity: an application to cubic and tetragonal inorganic compounds cites this paper.

High-throughput computational framework for lattice dynamics and thermal transport including high-order anharmonicity: an application to cubic and tetragonal inorganic compounds Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:52:01.516685Z

Source-reported events for the cited work

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Observation 7edd7867-f8ee-4150-873e-282c7598869f · inbound

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

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:05:10.085967Z

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Observation b0041fc3-ac8c-4df0-8405-9d3f22412c24 · inbound

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects cites this paper.

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:41:52.872922Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 47360736-74bb-4eac-bf51-069b7321a8fd · inbound

Pushing the limits of unconstrained machine-learned interatomic potentials cites this paper.

Pushing the limits of unconstrained machine-learned interatomic potentials Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T08:46:43.771367Z

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Observation 0c116c5e-2797-4909-bc7a-6c813550b5ee · inbound

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures cites this paper.

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:10.914921Z

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Observation f4d89109-9153-4d42-aa5a-dfd970dd5044 · inbound

Performance of universal machine learning potentials in global optimization of inorganic crystal structures cites this paper.

Performance of universal machine learning potentials in global optimization of inorganic crystal structures Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T20:24:17.162723Z

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Observation ad15ccd1-42f1-4a55-82d4-91d420afb58f · inbound

Accelerating point defect simulations using data-driven and machine learning approaches cites this paper.

Accelerating point defect simulations using data-driven and machine learning approaches Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:06:05.219579Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1f1c316a-dc4e-463f-8abf-4cb23f6ed0dc · inbound

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

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.135527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a5cc641c-e452-4ae3-a9ef-7d7fa4c6bc04 · inbound

Approaching the Limit of Intrinsic Crystalline Thermal Insulation cites this paper.

Approaching the Limit of Intrinsic Crystalline Thermal Insulation Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-03T06:57:42.912193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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

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

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 f6c2a381-97a5-4fdf-9dfc-8f0882beafe2 · inbound

Transformer Atomic Cluster Expansion: TRACE cites this paper.

Transformer Atomic Cluster Expansion: TRACE Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T01:54:38.712965Z

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Observation 3a10b3b9-1076-4bfd-838f-08b338caa290 · inbound

Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials cites this paper.

Fast and Accurate Foundation Models for Equivariant Machine-Learned Interatomic Potentials Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T06:41:38.835017Z

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