Paper Citation Record · LEDGER

High-performance training and inference for deep equivariant interatomic potentials

As of 17 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 14 inbound Pith citation observations for arXiv:2504.16068.

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

pith.paper-citation-record.v1
2504.16068 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:13:44.485270Z

measured 78 of 78 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:09:26.190324Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

64 of 64 outbound references displayed

verified exact2
verified fuzzy18
unresolved44
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 821cfa17-e291-42d1-b55c-2f1a33f41404 · outbound

This paper cites Behler and M.

High-performance training and inference for deep equivariant interatomic potentials Behler and M

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.178720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f9fa22-fbe6-4ba8-934e-7593af77f00f · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.827336Z

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 e3318fe7-d070-45cd-896a-b6cf6b2a6423 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.816148Z

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 b3790434-3adf-40b7-9c16-021c12232ec3 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.805421Z

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 40d7f71c-0caf-4c9c-bb2f-a0e74fa330fd · outbound

This paper cites Lysogorskiy, C.

High-performance training and inference for deep equivariant interatomic potentials Lysogorskiy, C

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.795040Z

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 5721a207-681a-4936-900d-6370b910b071 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.693865Z

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 7459e4c8-f134-4552-8fa7-40f70375f864 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.682965Z

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 9fae85ce-bd3c-4dee-81ea-fe58d2200e5a · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.671848Z

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 74239db3-cef5-4046-91d7-ff557e17f1e3 · outbound

This paper cites Podryabinkin, K.

High-performance training and inference for deep equivariant interatomic potentials Podryabinkin, K

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.567412Z

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 64cf63da-818b-486b-b90e-4f3b1578abb8 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.555785Z

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 2ee6041a-b8ce-4a31-b439-5e4fd7a6c7e7 · outbound

This paper cites Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE.

High-performance training and inference for deep equivariant interatomic potentials Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.416218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ad3a2bb-2fce-42dd-be23-ded7e036df3d · outbound

This paper cites Eastman, P.

High-performance training and inference for deep equivariant interatomic potentials Eastman, P

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.519089Z

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 fd533363-19f4-47ae-9437-65655041db89 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.389122Z

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 1d82f87b-416f-40dc-886a-a08df95f085f · outbound

This paper cites Schmidt, T.

High-performance training and inference for deep equivariant interatomic potentials Schmidt, T

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.350071Z

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 81bd7e73-0d5f-4348-a411-0e9bb9014bd0 · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

High-performance training and inference for deep equivariant interatomic potentials Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.544759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ba73cbf-c26c-42e0-8794-f3ee1c74ad99 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.549470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f75fcd65-5443-4a49-ac45-b9d7277e1920 · outbound

This paper cites Chen and S.

High-performance training and inference for deep equivariant interatomic potentials Chen and S

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.553217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9f58be3-2faa-47f0-8385-95708eebe50c · outbound

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

High-performance training and inference for deep equivariant interatomic potentials MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.556588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf6e25e2-ba77-4eaf-9f51-6587c8d5d7db · outbound

This paper cites A foundation model for atomistic materials chemistry.

High-performance training and inference for deep equivariant interatomic potentials A foundation model for atomistic materials chemistry

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.619061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26be16c3-6795-46b3-9ac3-619c6564d253 · outbound

This paper cites Merchant, S.

High-performance training and inference for deep equivariant interatomic potentials Merchant, S

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.670169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22316934-b210-49c8-85c4-b0f5d8f0be64 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:46.326518Z

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 3d485229-22f9-4eab-822c-e8ccf619bc9b · outbound

This paper cites Eastman, B.

High-performance training and inference for deep equivariant interatomic potentials Eastman, B

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.315802Z

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 4493fce2-fe28-4d57-962c-d1fe8c4264f8 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

High-performance training and inference for deep equivariant interatomic potentials MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.681040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73773083-bc7d-4c49-948c-5f07cd667120 · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

High-performance training and inference for deep equivariant interatomic potentials Orb: A Fast, Scalable Neural Network Potential

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.684892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcec7ad1-2c50-4122-a5c2-15c64905d4fa · outbound

This paper cites Orb-v3: atomistic simulation at scale.

High-performance training and inference for deep equivariant interatomic potentials Orb-v3: atomistic simulation at scale

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.764968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2642ff7-4468-49c9-8127-5bdca3b11d4d · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

High-performance training and inference for deep equivariant interatomic potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.819803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea36cfd2-4faa-499d-8499-61e443e24831 · outbound

This paper cites Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics.

High-performance training and inference for deep equivariant interatomic potentials Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.823878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a75f5ad7-a551-43ac-acb8-f6a1d803d628 · outbound

This paper cites Mosquera-Lois, S.

High-performance training and inference for deep equivariant interatomic potentials Mosquera-Lois, S

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.304481Z

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 00f9cf5b-d2b4-4b9f-8297-57492c9c5ddb · outbound

This paper cites Screening of material defects using universal machine-learning interatomic potentials.

High-performance training and inference for deep equivariant interatomic potentials Screening of material defects using universal machine-learning interatomic potentials

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.830850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 200b91b7-38c2-4f94-bb69-e9970313c5ec · outbound

This paper cites Batzner, A.

High-performance training and inference for deep equivariant interatomic potentials Batzner, A

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.292428Z

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 24295c1f-e5a7-4e69-a24b-4adf70b785ae · outbound

This paper cites Musaelian, S.

High-performance training and inference for deep equivariant interatomic potentials Musaelian, S

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.837334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9232e4da-c06f-4022-8bd1-6f525817ed50 · outbound

This paper cites Batatia, D.

High-performance training and inference for deep equivariant interatomic potentials Batatia, D

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.159979Z

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 8e21ff1b-64cc-4ab0-bf95-1e3a33f0d49f · outbound

This paper cites Bochkarev, Y.

High-performance training and inference for deep equivariant interatomic potentials Bochkarev, Y

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.067427Z

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 dd6615bd-a3fc-4c50-b7d6-e382f313882e · outbound

This paper cites Paszke, S.

High-performance training and inference for deep equivariant interatomic potentials Paszke, S

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:43.848908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b276b8d4-02ea-4adc-ae9e-956f9cfb37ce · outbound

This paper cites Ansel, E.

High-performance training and inference for deep equivariant interatomic potentials Ansel, E

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.050018Z

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 de1cef73-7bee-4a03-a9d4-877521b663d8 · outbound

This paper cites Tillet, H.-T.

High-performance training and inference for deep equivariant interatomic potentials Tillet, H.-T

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.038104Z

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 b04fefc7-fdff-4ba5-935e-59b9ce133c09 · outbound

This paper cites Dagum and R.

High-performance training and inference for deep equivariant interatomic potentials Dagum and R

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:46.027733Z

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 65b6f77f-29ea-4fc4-8d59-3d2313534e4f · outbound

This paper cites TorchBench: Benchmarking PyTorch with High API Surface Coverage.

High-performance training and inference for deep equivariant interatomic potentials TorchBench: Benchmarking PyTorch with High API Surface Coverage

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.042068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89dd264b-c998-430c-9504-7d39b2c72d3f · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.903289Z

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 cc0e6be4-fd51-4e65-b745-dd855a73ee91 · outbound

This paper cites Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition.

High-performance training and inference for deep equivariant interatomic potentials Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.049249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abf53fbf-d52e-4ab0-9c13-5b3457c159f4 · outbound

This paper cites Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.

High-performance training and inference for deep equivariant interatomic potentials Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.053281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81225308-d1a6-4573-b63d-b71ba177708c · outbound

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

High-performance training and inference for deep equivariant interatomic potentials Universal Machine Learning Interatomic Potentials are Ready for Phonons

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.057213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d413419d-8af0-4c11-b60a-8277b41c2c29 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.061029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94fc5099-62cb-4ec2-a8c9-decc92f04959 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.748484Z

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 7665f0d1-02f8-47d2-8469-c4b910000a91 · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

High-performance training and inference for deep equivariant interatomic potentials PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.068421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e924a548-c09d-40c5-9573-3e87c5c06d12 · outbound

This paper cites Falcon and The PyTorch Lightning team, PyTorch Lightning (2019).

High-performance training and inference for deep equivariant interatomic potentials Falcon and The PyTorch Lightning team, PyTorch Lightning (2019)

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:45.709149Z

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 246f75c2-e661-4d8d-8fb1-340f8a096375 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.158123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39cee2b0-c1cb-4030-a808-9c7b4b63277a · outbound

This paper cites A predictive machine learning force field framework for liquid electrolyte development.

High-performance training and inference for deep equivariant interatomic potentials A predictive machine learning force field framework for liquid electrolyte development

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.224851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c921501-2111-4da2-893f-cae09adef462 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.690919Z

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 8e97dc2d-a421-4f26-bd98-8193a3e97d50 · outbound

This paper cites Kozinsky, A.

High-performance training and inference for deep equivariant interatomic potentials Kozinsky, A

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:45.680625Z

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 88a877ce-d7be-4c13-adfd-4260acecf072 · outbound

This paper cites Using Python for Model Inference in Deep Learning.

High-performance training and inference for deep equivariant interatomic potentials Using Python for Model Inference in Deep Learning

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:13:44.864310Z

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 979604e0-c513-429c-b42d-210b18b8528b · outbound

This paper cites OpenMM-Python-Force: Deploying Accelerated Python Modules in Molecular Dynamics Simulation.

High-performance training and inference for deep equivariant interatomic potentials OpenMM-Python-Force: Deploying Accelerated Python Modules in Molecular Dynamics Simulation

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:13:44.712590Z

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 3ea39e00-3241-4957-bc49-03fc4510f0a4 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.624660Z

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 55dc748c-b282-4323-a01a-d41096ff3b68 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.433130Z

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 1f26451d-84ea-465b-804b-80970f8ddf55 · outbound

This paper cites Liger Kernel: Efficient Triton Kernels for LLM Training.

High-performance training and inference for deep equivariant interatomic potentials Liger Kernel: Efficient Triton Kernels for LLM Training

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.313376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e282f17-5f06-4f7b-a066-cec0dd261ae8 · outbound

This paper cites An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks.

High-performance training and inference for deep equivariant interatomic potentials An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.387568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e575f677-7627-451d-9cee-d230dd60d49f · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.423013Z

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 6567f93b-0c29-4afa-abe0-a60ec36e1d35 · outbound

This paper cites Najibi and L.

High-performance training and inference for deep equivariant interatomic potentials Najibi and L

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:45.411035Z

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 a33d439b-ba21-4d77-b874-0960af168850 · outbound

This paper cites Mardirossian and M.

High-performance training and inference for deep equivariant interatomic potentials Mardirossian and M

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:45.399692Z

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 2a0b4605-920b-42b2-af9d-0ad0e9a12374 · outbound

This paper cites Hattori and Q.

High-performance training and inference for deep equivariant interatomic potentials Hattori and Q

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:45.387614Z

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 e6b7269a-25ab-48f8-be55-b409a2880c6c · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T11:13:44.474988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90072f1f-3475-4d9c-95a6-9c9eb57f71b9 · outbound

This paper cites Barnett and J.

High-performance training and inference for deep equivariant interatomic potentials Barnett and J

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:13:45.285624Z

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 314d9d73-8733-43ab-b543-fbe7f38b619c · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.167400Z

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 a0707e31-daea-496c-9f08-112d3b4ecff2 · outbound

This paper cites an unresolved cited work.

High-performance training and inference for deep equivariant interatomic potentials Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:13:45.155881Z

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.

Pith citing papers

Observation 169e44ba-e5e9-4555-ba12-af356f5ee05f · inbound

Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics cites this paper.

Identifying Split Vacancy Defects with Machine-Learned Foundation Models and Electrostatics High-performance training and inference for deep equivariant interatomic potentials

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T00:46:41.565818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21dc6f88-0ee9-4065-9b92-361b020f3330 · inbound

An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks cites this paper.

An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks High-performance training and inference for deep equivariant interatomic potentials

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-10T15:59:39.057623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5e19e01-c2d8-4d03-980f-e6ef5a15be94 · inbound

The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products cites this paper.

The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products High-performance training and inference for deep equivariant interatomic potentials

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:09:26.190324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41319b3b-fc24-4cd5-8ad7-c2bbc90a89c3 · inbound

Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes cites this paper.

Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes High-performance training and inference for deep equivariant interatomic potentials

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T23:48:00.812931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeab3395-734a-4455-bd95-6a8f4bd1d3ec · inbound

Physics-Constrained Self-Energy Warm Starts for Charge-Self-Consistent DFT+DMFT: Application to Iron at Core Conditions cites this paper.

Physics-Constrained Self-Energy Warm Starts for Charge-Self-Consistent DFT+DMFT: Application to Iron at Core Conditions High-performance training and inference for deep equivariant interatomic potentials

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-21T17:04:16.184813Z

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 f1253b2a-3736-4398-ae43-a0bcfbc62ec8 · 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 High-performance training and inference for deep equivariant interatomic potentials

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:06:05.199199Z

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 7be39d3a-5583-4b84-8b0f-763f38fc2b65 · inbound

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning cites this paper.

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning High-performance training and inference for deep equivariant interatomic potentials

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:14.090555Z

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 75d6fbaa-c0b3-4c5a-8e8a-c8da1c9d085f · inbound

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials cites this paper.

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials High-performance training and inference for deep equivariant interatomic potentials

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-19T23:52:52.986072Z

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 a88086b0-a17d-4513-8358-fcdb9b3db30d · inbound

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials cites this paper.

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials High-performance training and inference for deep equivariant interatomic potentials

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:38:10.420297Z

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 72dfa6d7-f0cc-4ca7-9852-934325230acc · inbound

Anomalous Subsurface Vacancy Stabilization Dictated by Geometry-Electronic Decoupling on Metal Surfaces cites this paper.

Anomalous Subsurface Vacancy Stabilization Dictated by Geometry-Electronic Decoupling on Metal Surfaces High-performance training and inference for deep equivariant interatomic potentials

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:13:59.078446Z

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 2fd8273f-089d-49ca-8c73-153595d1992d · inbound

Using graph neural networks to predict many-body interactions in amorphous materials cites this paper.

Using graph neural networks to predict many-body interactions in amorphous materials High-performance training and inference for deep equivariant interatomic potentials

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:32:37.367090Z

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 b615bc21-33c0-4a7e-9952-f140c948ae3a · inbound

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

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models High-performance training and inference for deep equivariant interatomic potentials

Reference 240

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.027367Z

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 ab7f6c2d-6d62-4885-b2d2-74a6ebb56765 · inbound

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning cites this paper.

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning High-performance training and inference for deep equivariant interatomic potentials

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:49:03.264092Z

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 be17adeb-0c71-4b0a-a38b-3ae4fd8f530e · 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 High-performance training and inference for deep equivariant interatomic potentials

Reference 45

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.