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Paper Citation Record · LEDGER

Self-Refining Training for Amortized Density Functional Theory

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.01225.

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

pith.paper-citation-record.v1
2506.01225 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:56:08.989102Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 75758617-c814-4d9f-808d-4d47eb7e717e · outbound

This paper cites Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

Self-Refining Training for Amortized Density Functional Theory Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Reference 1

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Source-reported events for the cited work

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Observation 97e688ba-4e71-465b-96f7-709622532b85 · outbound

This paper cites and Savaré, G.

Self-Refining Training for Amortized Density Functional Theory and Savaré, G

Reference 2

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Observation 0a018b8e-6728-43a6-a695-754b15758675 · outbound

This paper cites Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

Self-Refining Training for Amortized Density Functional Theory Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 3

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Observation db6512dc-9324-45b2-b206-eca99997d19e · outbound

This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Self-Refining Training for Amortized Density Functional Theory J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 63e142f0-2baa-4271-af1b-c7ed79c36678 · outbound

This paper cites E., Poltavsky, I., Schütt, K.

Self-Refining Training for Amortized Density Functional Theory E., Poltavsky, I., Schütt, K

Reference 5

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Observation 3e3a741a-d0aa-4634-be0c-31df5ce61b5c · outbound

This paper cites T., and Vargas-Hern \'a ndez, R.

Self-Refining Training for Amortized Density Functional Theory T., and Vargas-Hern \'a ndez, R

Reference 6

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Observation 65528e30-a133-4ab3-9056-1ead650f7340 · outbound

This paper cites an unresolved cited work.

Self-Refining Training for Amortized Density Functional Theory Unresolved cited work

Reference 7

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Observation d7f3a4e4-1bfb-455e-a1f0-11a574b9c54d · outbound

This paper cites W., Musaelian, A., Owen, C.

Self-Refining Training for Amortized Density Functional Theory W., Musaelian, A., Owen, C

Reference 8

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Observation 4f45defc-8db2-49e1-a47d-1c5d414c3eb0 · outbound

This paper cites J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.

Self-Refining Training for Amortized Density Functional Theory J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y

Reference 9

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Source-reported events for the cited work

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Observation 6c74d9e3-bdce-4f14-881b-a282382c107a · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Self-Refining Training for Amortized Density Functional Theory Explaining and Harnessing Adversarial Examples

Reference 10

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Unavailable: canonical work link unavailable.

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Observation ad241622-0940-41e2-9fbd-4a596baae852 · outbound

This paper cites ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation.

Self-Refining Training for Amortized Density Functional Theory ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation

Reference 11

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Observation a5958aa6-f04d-438c-aa57-cc8f17c7d605 · outbound

This paper cites and Pople, J.

Self-Refining Training for Amortized Density Functional Theory and Pople, J

Reference 12

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 781526d6-e8ac-4e10-8a51-cddb5ea44532 · outbound

This paper cites J., Stewart, R.

Self-Refining Training for Amortized Density Functional Theory J., Stewart, R

Reference 13

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Observation 937602c5-c325-45ef-a2d6-d71cac1a483f · outbound

This paper cites MESS: Modern Electronic Structure Simulations.

Self-Refining Training for Amortized Density Functional Theory MESS: Modern Electronic Structure Simulations

Reference 14

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Observation 056581f6-687a-454a-8459-958f5b53c100 · outbound

This paper cites and Kohn, W.

Self-Refining Training for Amortized Density Functional Theory and Kohn, W

Reference 15

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 59d6b6cc-8b12-4387-8da2-abb7996b36ef · outbound

This paper cites and Kohn, W.

Self-Refining Training for Amortized Density Functional Theory and Kohn, W

Reference 16

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 6853988b-2a38-4170-b04d-62aae34119e8 · outbound

This paper cites DQC: a Python program package for Differentiable Quantum Chemistry.

Self-Refining Training for Amortized Density Functional Theory DQC: a Python program package for Differentiable Quantum Chemistry

Reference 17

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Observation b62accf1-b9cd-4ce8-926c-1f1a40185703 · outbound

This paper cites A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., and Tkatchenko, A.

Self-Refining Training for Amortized Density Functional Theory A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., and Tkatchenko, A

Reference 18

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Observation 0f67e186-a66e-43b0-b61c-dd82c6706254 · outbound

This paper cites and Sham, L.

Self-Refining Training for Amortized Density Functional Theory and Sham, L

Reference 19

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Observation 303ee3cd-ae2a-4cd6-86e1-f46bbb726634 · outbound

This paper cites and Sham, L.

Self-Refining Training for Amortized Density Functional Theory and Sham, L

Reference 20

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Observation 6a46fca7-b779-4b0f-8e5d-cb7100386e99 · outbound

This paper cites and Matthews, C.

Self-Refining Training for Amortized Density Functional Theory and Matthews, C

Reference 21

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 020dfa80-7edc-44bd-bbed-953122bdd2af · outbound

This paper cites D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory.

Self-Refining Training for Amortized Density Functional Theory D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory

Reference 22

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ca613a3c-6bfe-4d55-a1f5-d34ff7350033 · outbound

This paper cites Neural-network Density Functional Theory Based on Variational Energy Minimization.

Self-Refining Training for Amortized Density Functional Theory Neural-network Density Functional Theory Based on Variational Energy Minimization

Reference 23

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f1f97221-55b1-4f60-8f46-221764c16269 · outbound

This paper cites Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory.

Self-Refining Training for Amortized Density Functional Theory Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory

Reference 24

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Observation 92953c36-bb93-41d0-914e-407d6cbeab9e · outbound

This paper cites and Shimazaki, T.

Self-Refining Training for Amortized Density Functional Theory and Shimazaki, T

Reference 25

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Observation 25ca7912-1424-4bec-aef0-539883c6c95f · outbound

This paper cites Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning.

Self-Refining Training for Amortized Density Functional Theory Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning

Reference 26

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Observation 474867f6-bc8b-4c16-9f33-6578430d3291 · outbound

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Self-Refining Training for Amortized Density Functional Theory Unresolved cited work

Reference 27

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Observation dad7b659-0cac-4334-ab03-d609585805e4 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Self-Refining Training for Amortized Density Functional Theory PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 28

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:56:08.944456Z digest=sha256:b91472c167e83d2d96642784eef08cb777cd5a19cce895bb0714e8a622467f28

Observation 6758acc6-2c96-4e70-8e66-5d1da3e28498 · outbound

This paper cites C., Teter, M.

Self-Refining Training for Amortized Density Functional Theory C., Teter, M

Reference 29

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 2f6418bc-0803-4442-acd6-f4ade2f18a70 · outbound

This paper cites P., Burke, K., and Ernzerhof, M.

Self-Refining Training for Amortized Density Functional Theory P., Burke, K., and Ernzerhof, M

Reference 30

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 05a581fb-f6c8-4e4b-a4d5-d88462dcf9e9 · outbound

This paper cites The Effectiveness of Data Augmentation in Image Classification using Deep Learning.

Self-Refining Training for Amortized Density Functional Theory The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Reference 31

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Unavailable: canonical work link unavailable.

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Observation e66425ea-f4b7-4fc5-8562-e3e0817ca0f4 · outbound

This paper cites A., and Burke, K.

Self-Refining Training for Amortized Density Functional Theory A., and Burke, K

Reference 32

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 17d7ea88-8766-45f8-863a-a810d3ea6967 · outbound

This paper cites O., Rupp, M., and von Lilienfeld, O.

Self-Refining Training for Amortized Density Functional Theory O., Rupp, M., and von Lilienfeld, O

Reference 33

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9e9a5a21-c46c-4c3e-9a7c-4521e0b96a80 · outbound

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Self-Refining Training for Amortized Density Functional Theory Unresolved cited work

Reference 34

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Observation 36457117-765a-46a7-a781-5998ad24273c · outbound

This paper cites Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions.

Self-Refining Training for Amortized Density Functional Theory Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions

Reference 35

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local_arxiv, observed 2026-08-07T11:56:09.059836Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 75bd3a76-9a82-4755-9b9d-3af41473121c · outbound

This paper cites T., Sauceda, H.

Self-Refining Training for Amortized Density Functional Theory T., Sauceda, H

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:56:09.185084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 234eaa2d-7874-4e26-9f8c-8bcdeddc6d63 · outbound

This paper cites C., Blunt, N.

Self-Refining Training for Amortized Density Functional Theory C., Blunt, N

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T11:56:08.969921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:56:08.969921Z digest=sha256:25e513ba54044a443f4c6bea3a6de77a172b7fe63fea112dfcd81d5709cbd617

Observation 0bbf6ac5-09fd-4cc9-9bd5-03f720495f2b · outbound

This paper cites TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials.

Self-Refining Training for Amortized Density Functional Theory TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T11:56:08.972797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:56:08.972797Z digest=sha256:85d35f290e61fcaa5964854aa1d5c069350b32b5e94e4d8af798e0495f79bafe

Observation bf46cadc-0158-4a40-b7df-94654cc53907 · outbound

This paper cites T., Chmiela, S., Sauceda, H.

Self-Refining Training for Amortized Density Functional Theory T., Chmiela, S., Sauceda, H

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:56:09.178159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:56:08.975763Z digest=sha256:5b35caf0a72cd83f17d1a6fa5d4cb7ef5f08b183e95938fcd3d5b9463162ab9b

Observation 6a2b629c-3509-41cf-aa4f-a93eaa04cbdf · outbound

This paper cites an unresolved cited work.

Self-Refining Training for Amortized Density Functional Theory Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:56:09.171077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:56:08.978107Z digest=sha256:3b717bc2b2336558efbdca5f96fab8cd3a8770cf5bb5c2d3a01077467c874edd

Observation 71c07535-a056-4d9d-8fea-8891b64453be · outbound

This paper cites Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method.

Self-Refining Training for Amortized Density Functional Theory Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:56:09.039056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:56:08.980369Z digest=sha256:0d33bfe510fbb107102ce3da00d244f5116bb993d303713cb2d066813a766101

Observation 32704b68-34d2-453e-a226-49f2320da945 · outbound

This paper cites Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian.

Self-Refining Training for Amortized Density Functional Theory Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T11:56:08.982823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:56:08.982823Z digest=sha256:f70756835cf946c631a069266f68c7e93d85bd09764de5ee30ecc348e11f6a46

Observation 0545744d-4ca6-423f-a741-a8dd453b8608 · outbound

This paper cites Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction.

Self-Refining Training for Amortized Density Functional Theory Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:56:09.018894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:56:08.986486Z digest=sha256:37d727966d749b7c2c541fbfa0d8e2dfa95da4cc926f7b72b41578a1d8e1f106

Observation 77e0e673-49b6-4c7e-8f27-ebabb8148879 · outbound

This paper cites an unresolved cited work.

Self-Refining Training for Amortized Density Functional Theory Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:56:09.164089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-07T11:56:08.989102Z digest=sha256:376dfd382b763a671cefb68a8c50b2ae1ab2f9e8b77d1214bbfc8428e6cc06fd

Pith citing papers

No inbound Pith citation observations are available.