Paper Citation Record · LEDGER

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps

As of 16 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2412.18633.

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

pith.paper-citation-record.v1
2412.18633 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:19:25.303931Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:24.547504Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:13:24.929037Z

Reference resolution

45 of 45 outbound references displayed

verified exact3
verified fuzzy12
unresolved30
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c94d2c4f-3116-4433-b002-63850dfd151b · outbound

This paper cites MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.125669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c900ee-211a-4e74-825b-811228d1ff34 · outbound

This paper cites & Parrinello, M.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Parrinello, M

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.130619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c873d273-49a6-4757-afe1-ce0c44c3cbb0 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.818359Z

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

Observation a1a23f97-06ee-440e-91ef-631ffa4f9e00 · outbound

This paper cites W., Xu, L.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps W., Xu, L

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.807538Z

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 770d1494-f0f7-4c00-b0a7-37891bebdcec · outbound

This paper cites P., Payne, M.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps P., Payne, M

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1f55670-a1e3-453e-80d5-4e4159bb471d · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.796113Z

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 38ab5121-367e-4bcb-9f35-71a46258a48d · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.151240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f59dfcc-0b83-4028-be82-148ac027734b · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.784330Z

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 ede16b8b-3b5a-47f9-b4e7-d1ba9d264b78 · outbound

This paper cites & Ceriotti, M.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Ceriotti, M

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.772827Z

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 fda578eb-1670-4af8-9ccc-9e5b81c50e47 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.761280Z

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Observation e828e7ca-7a20-466d-ae61-18ff22c1eb7a · outbound

This paper cites P., Simm, G.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps P., Simm, G

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.749868Z

Source-reported events for the cited work

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Observation 9a26c9ce-46c9-42d1-a515-c9880c1d123f · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.738074Z

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

Observation bcfbf844-bde9-4ebf-8259-fc395d5c4afe · outbound

This paper cites L., Fako, E., De, S., Schäfer, A.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps L., Fako, E., De, S., Schäfer, A

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.726699Z

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 24015bb8-ba0c-4b3b-9ea7-637199c6277d · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.714102Z

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 a1df1f4e-9553-4849-909d-c7c8be122d2a · outbound

This paper cites & Deringer, V.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Deringer, V

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.702522Z

Source-reported events for the cited work

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Observation c9bc10c3-02be-431b-9350-35b5a4f9954c · outbound

This paper cites A foundation model for atomistic materials chemistry.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps A foundation model for atomistic materials chemistry

Reference 16

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Source-reported events for the cited work

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Observation 50fe2aa2-888f-495c-9812-039b50d5d2f7 · outbound

This paper cites Zero Shot Molecular Generation via Similarity Kernels.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Zero Shot Molecular Generation via Similarity Kernels

Reference 17

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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 b74b565d-9db4-475f-999b-34051f540ce3 · outbound

This paper cites & Harvey, J.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Harvey, J

Reference 18

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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 29ad9f31-2fbc-4b4d-aad0-d59fff178e4f · outbound

This paper cites & Laio, A.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Laio, A

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.679784Z

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 c8d4fab7-2a90-404a-9594-de56bccdb185 · outbound

This paper cites Umbrella sampling.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Umbrella sampling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.205433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5c396de-3ead-4c44-b932-14d11a522b1d · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.666574Z

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

Observation e983ece7-f9d1-41f6-9d14-178303627f37 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.213220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fe1e4c7-6666-4c85-9502-33db776784a2 · outbound

This paper cites Learning Interatomic Potentials at Multiple Scales.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Learning Interatomic Potentials at Multiple Scales

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.216860Z

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Unavailable: canonical work link unavailable.

Observation 57df20ea-0cd4-4a97-ac08-2ed7ee056e73 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.221571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb581d84-4185-487e-9150-493d6c6b0b05 · outbound

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

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.225279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e9f900e-bffd-4173-a59e-6148dd14caf9 · outbound

This paper cites Transferable Boltzmann Generators.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Transferable Boltzmann Generators

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.229264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c2f60f9-b064-41f8-bf59-51a10487f5ca · outbound

This paper cites Flow Annealed Importance Sampling Bootstrap.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Flow Annealed Importance Sampling Bootstrap

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.234414Z

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Unavailable: canonical work link unavailable.

Observation b214518b-ddc6-4c34-a815-9c97ddccff40 · outbound

This paper cites SE(3) Equivariant Augmented Coupling Flows.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps SE(3) Equivariant Augmented Coupling Flows

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:19:25.452801Z

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 961329af-46c8-4ed6-a642-08d6f287fa42 · outbound

This paper cites Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.242112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9775adf-13ab-46b4-87db-1be4000c92af · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.653628Z

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 a27131fc-3f36-47f3-83b1-a1820324677c · outbound

This paper cites Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.249889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 340b2641-085c-4488-bcfa-2263c2b1552d · outbound

This paper cites e3nn: Euclidean Neural Networks.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps e3nn: Euclidean Neural Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.253977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6de61d3b-7c81-4b1e-9ad6-cb198955e84d · outbound

This paper cites Lie groups and Lie algebras 247-267 (Elements of the History of Mathematics, 1994).

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Lie groups and Lie algebras 247-267 (Elements of the History of Mathematics, 1994)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.642063Z

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 b96b13a2-3357-4e46-b55b-e48371051071 · outbound

This paper cites A solution for the best rotation to relate two sets of vectors.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps A solution for the best rotation to relate two sets of vectors

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.630823Z

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 4b9b2ae3-8c26-4652-b246-c1a115c07a1b · outbound

This paper cites The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.265738Z

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Unavailable: canonical work link unavailable.

Observation 0562804f-873b-4e0e-afc9-6ffb3ae12f6b · outbound

This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.270329Z

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Unavailable: canonical work link unavailable.

Observation ad1d4015-a2a4-4991-8721-6e96dfb74ac1 · outbound

This paper cites & Csányi, G.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Csányi, G

Reference 37

Resolution: verified exact; doi, observed 2026-08-11T10:19:25.345639Z

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 8605b509-097f-4bd4-860c-c136954a3e2e · outbound

This paper cites & Leimkuhler, B.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Leimkuhler, B

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.618029Z

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 2ecb1167-c41d-4a27-80aa-34793228a5e6 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.605424Z

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 44e3a978-e4e9-4b31-a59c-db73d9cc3dac · outbound

This paper cites & Rao, F.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps & Rao, F

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.593270Z

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 fc1583c8-bb57-49d1-8098-8a84978171c7 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.291416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14af8b15-eb3e-4220-8374-78f62a7d2034 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.582000Z

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 4470f3f4-64c9-418e-831a-ff7ff2ec90e6 · outbound

This paper cites an unresolved cited work.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:19:25.570367Z

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 c81c2e87-0e63-4a37-a634-44c4a8f6271a · outbound

This paper cites h(t) i = R¯θi (Hi,k) + X t′ MLP(t′)(h(t′) i ) X j |xij| (12) The original readout of the reference model Rt can then be used to compute the energy as E(t) i = Rt h(t) i.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps h(t) i = R¯θi (Hi,k) + X t′ MLP(t′)(h(t′) i ) X j |xij| (12) The original readout of the reference model Rt can then be used to compute the energy as E(t) i = Rt h(t) i

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:19:25.557485Z

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 8d5dbdb0-280c-4e47-a9e9-2055c3914b3f · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T10:19:25.169346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation dd4e5369-b7d7-41f9-9eaf-e1279c91c024 · inbound

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems cites this paper.

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:13:24.969953Z

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.