Paper Citation Record · LEDGER

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

As of 2 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2604.26143.

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

pith.paper-citation-record.v1
2604.26143 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-07T13:50:56.400576Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-02T06:30:47.504484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T06:41:42.686432Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

verified exact4
verified fuzzy20
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 443faa2b-1230-475d-a7dc-ea4d7d2a56c1 · outbound

This paper cites The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:46:25.814745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation efb5d777-d009-4956-8b07-c9def3747904 · outbound

This paper cites E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature communica- tions, 13(1):2453.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature communica- tions, 13(1):2453

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.802131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation dfcf3955-cf3e-41af-a39e-a7f80f659edc · outbound

This paper cites Mace: Higher order equiv- ariant message passing neural networks for fast and ac- curate force fields.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Mace: Higher order equiv- ariant message passing neural networks for fast and ac- curate force fields

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.777205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 2647dad3-7bb8-41bb-8d42-7728553f51e4 · outbound

This paper cites Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing.Physical Review X, 14(2):021036.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing.Physical Review X, 14(2):021036

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.807422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b3caac42-7096-40bc-af0a-d6f2ed0e319a · outbound

This paper cites Warshel and M.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Warshel and M

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.828484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 43873a61-f953-43a5-94a3-926409a3b237 · outbound

This paper cites Hybrid atomistic simulation methods for materials sys- tems.Reports on Progress in Physics, 72(2):026501.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Hybrid atomistic simulation methods for materials sys- tems.Reports on Progress in Physics, 72(2):026501

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.825005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a6d35cb0-c01a-4561-8540-9895dc21bfc3 · outbound

This paper cites Mixture of diverse size experts.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Mixture of diverse size experts

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.831949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation f8017669-f2d5-4f36-81b8-ec50cef67664 · outbound

This paper cites an unresolved cited work.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-27T05:08:49.793240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 7bb6108a-e32f-47a6-b821-28ca9f1868a0 · outbound

This paper cites Hmoe: Heterogeneous mixture of 10 experts for language modeling.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Hmoe: Heterogeneous mixture of 10 experts for language modeling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.803907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation e70498d9-0119-4568-82ff-297d9e85406d · outbound

This paper cites Swinburne, and James R.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Swinburne, and James R

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.810499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 96e47565-3972-4f31-a7de-9cc1317ca6e1 · outbound

This paper cites Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon.npj Computational Materials, 7(1):97, June 2021.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon.npj Computational Materials, 7(1):97, June 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.817733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation caa7f746-c31d-4a85-803f-bba6f20498ce · outbound

This paper cites Xie, Matthias Rupp, and Richard G.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Xie, Matthias Rupp, and Richard G

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.835293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation f166cea8-9ca7-485e-9905-5c79a382ccbc · outbound

This paper cites The embedded-atom method: a review of theory and applications.Materials Science Reports, 9(7-8):251–310.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations The embedded-atom method: a review of theory and applications.Materials Science Reports, 9(7-8):251–310

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.781435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b4144777-1f2c-453e-92bc-15c3c93f9940 · outbound

This paper cites Adaptive-precision potentials for large-scale atomistic simulations.The Journal of Chemical Physics, 162(11).

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Adaptive-precision potentials for large-scale atomistic simulations.The Journal of Chemical Physics, 162(11)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.821185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 2e5d592e-a4e3-4c8d-b13e-386c8d96a41b · outbound

This paper cites Conservative adaptive-precision interatomic potentials, December 2025.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Conservative adaptive-precision interatomic potentials, December 2025

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:46:25.834792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 522242ee-999a-4c68-ad2b-d4a7a56155cc · outbound

This paper cites Kitchin, Daniel S.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Kitchin, Daniel S

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.839589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 8e94ed14-66c9-49e3-9a5c-a6c5ec9d8000 · outbound

This paper cites Scaling machine learning interatomic potentials with mixtures of experts.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Scaling machine learning interatomic potentials with mixtures of experts

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:46:25.829816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 8b22edde-f345-4995-a2db-dc32173badbb · outbound

This paper cites arXiv e-prints , keywords =.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations arXiv e-prints , keywords =

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:46:25.824895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 1433e574-6d3f-4cdf-83e0-d2392a6b793f · outbound

This paper cites Learning local equivariant representa- tions for large-scale atomistic dynamics.Nature Com- munications, 14(1):579.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Learning local equivariant representa- tions for large-scale atomistic dynamics.Nature Com- munications, 14(1):579

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.796713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 60cca3c2-d6ae-46d8-9ec4-725df6a83db1 · outbound

This paper cites Descoteaux, Mit Kotak, Gabriel de Miranda Nascimento, Se´ an R.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Descoteaux, Mit Kotak, Gabriel de Miranda Nascimento, Se´ an R

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.847463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 7ca5d79b-6cc0-4e75-8407-0469265f9fa0 · outbound

This paper cites Surface roughening in nanoparticle catalysts.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Surface roughening in nanoparticle catalysts

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:46:25.839715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b877a2ec-7fd7-49b3-bccf-1a52ef821ede · outbound

This paper cites On-the-fly active learning of interpretable bayesian force fields for atomistic rare events.npj Com- putational Materials, 6(1):20.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations On-the-fly active learning of interpretable bayesian force fields for atomistic rare events.npj Com- putational Materials, 6(1):20

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.814085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 825854e5-f08d-4483-a23d-bc1747609c6e · outbound

This paper cites Active learning of reactive bayesian force fields applied to heterogeneous catalysis dynamics of h/pt.Nature Communications, 13(1):5183.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Active learning of reactive bayesian force fields applied to heterogeneous catalysis dynamics of h/pt.Nature Communications, 13(1):5183

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.789587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b620b0c4-396e-4db8-8e3b-575e09644462 · outbound

This paper cites Efficiency of ab- initio total energy calculations for metals and semicon- ductors using a plane-wave basis set.Computational ma- terials science, 6(1):15–50.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Efficiency of ab- initio total energy calculations for metals and semicon- ductors using a plane-wave basis set.Computational ma- terials science, 6(1):15–50

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.806061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation c4f8cf57-5849-4e4f-aef1-56041c271291 · outbound

This paper cites Chemical accuracy for the van der waals density functional.Journal of Physics: Condensed Matter, 22(2):022201, dec 2009.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations Chemical accuracy for the van der waals density functional.Journal of Physics: Condensed Matter, 22(2):022201, dec 2009

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.843659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 69504d8a-88e9-438b-a5e8-7eae69f8244a · outbound

This paper cites The atomic simulation environ- ment—a python library for working with atoms.Journal of Physics: Condensed Matter, 29(27):273002.

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations The atomic simulation environ- ment—a python library for working with atoms.Journal of Physics: Condensed Matter, 29(27):273002

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T05:08:49.773088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Pith citing papers

Observation 3e526f97-e5d4-4e5b-a6e2-08d51a881c01 · 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 Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-31T06:41:42.686432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.