Paper Citation Record · LEDGER

Orb: A Fast, Scalable Neural Network Potential

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 44 inbound Pith citation observations for arXiv:2410.22570.

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

pith.paper-citation-record.v1
2410.22570 v1

Coverage vector

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measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 44 of 44 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:15:21.389974Z

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

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External citation measurements

43
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2b25fd6f-87ba-44c0-9e80-dedd438b1198 · inbound

A foundation model for atomistic materials chemistry cites this paper.

A foundation model for atomistic materials chemistry Orb: A Fast, Scalable Neural Network Potential

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:16:16.454760Z

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Observation 449f2ed9-5877-45d2-bd10-bd9ff3f3d60e · inbound

The OpenLAM Challenges cites this paper.

The OpenLAM Challenges Orb: A Fast, Scalable Neural Network Potential

Reference 4

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Observation ea152be6-16d4-49f8-99a1-f21d27e078b2 · inbound

Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms cites this paper.

Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms Orb: A Fast, Scalable Neural Network Potential

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:17:30.865770Z

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Observation ccfd4749-3338-4b0f-b4c1-6f8c633d4f43 · inbound

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys cites this paper.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Orb: A Fast, Scalable Neural Network Potential

Reference 24

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Observation 1484f640-967a-4ca2-92e7-08b41946c1b5 · inbound

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials cites this paper.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 93

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Observation f15212f8-6ebe-400d-b54e-d6a4a9990667 · inbound

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning cites this paper.

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning Orb: A Fast, Scalable Neural Network Potential

Reference 9

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Observation b2b76f11-8754-4a63-80b2-217e158ec763 · inbound

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors cites this paper.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Orb: A Fast, Scalable Neural Network Potential

Reference 29

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Observation 2d0a1dd7-d39b-4e51-93cf-106b666f3c19 · inbound

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data cites this paper.

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data Orb: A Fast, Scalable Neural Network Potential

Reference 21

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Observation 1e20601b-9ac8-4929-af1b-a7c6dadfad3e · inbound

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

Distillation of atomistic foundation models across architectures and chemical domains Orb: A Fast, Scalable Neural Network Potential

Reference 12

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Leveraging neural network interatomic potentials for a foundation model of chemistry cites this paper.

Leveraging neural network interatomic potentials for a foundation model of chemistry Orb: A Fast, Scalable Neural Network Potential

Reference 13

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Observation a392fc98-72b1-47f1-aaa7-6ddbbdd5235a · inbound

Universal Machine Learning Potential for Systems with Reduced Dimensionality cites this paper.

Universal Machine Learning Potential for Systems with Reduced Dimensionality Orb: A Fast, Scalable Neural Network Potential

Reference 9

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Source-reported events for the cited work

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Observation 0faa09e1-0447-40aa-bd79-4c0c0fafceba · inbound

Universal Machine Learning Potentials under Pressure cites this paper.

Universal Machine Learning Potentials under Pressure Orb: A Fast, Scalable Neural Network Potential

Reference 32

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Observation 01159521-e711-4f31-be6f-a30bad0d6ab2 · inbound

Benchmarking Universal Interatomic Potentials on Zeolite Structures cites this paper.

Benchmarking Universal Interatomic Potentials on Zeolite Structures Orb: A Fast, Scalable Neural Network Potential

Reference 22

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

Pushing the limits of unconstrained machine-learned interatomic potentials Orb: A Fast, Scalable Neural Network Potential

Reference 29

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Observation 3eb547f7-93d0-4e9d-b511-e65c1312003d · inbound

Thermodynamic assessment of machine learning models for solid-state synthesis prediction cites this paper.

Thermodynamic assessment of machine learning models for solid-state synthesis prediction Orb: A Fast, Scalable Neural Network Potential

Reference 2024

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Observation 8b40878d-34e7-4918-8793-6e86711d11d0 · inbound

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces cites this paper.

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Non-Conservative Forces Orb: A Fast, Scalable Neural Network Potential

Reference 57

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Observation 9a124cda-042b-4948-9e52-90cb4e36afcc · inbound

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

Performance of universal machine learning potentials in global optimization of inorganic crystal structures Orb: A Fast, Scalable Neural Network Potential

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T20:24:20.683588Z

Source-reported events for the cited work

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Observation f2caba35-74b5-4de2-bb4a-1de1cc860a0a · inbound

Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys cites this paper.

Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys Orb: A Fast, Scalable Neural Network Potential

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:28:17.661994Z

Source-reported events for the cited work

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Observation ff405e08-3cf4-45c7-a79c-0e8d14aca082 · inbound

OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent cites this paper.

OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent Orb: A Fast, Scalable Neural Network Potential

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:41:30.235916Z

Source-reported events for the cited work

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Observation 29cddac4-7994-47a9-a76f-e24488d32d7d · inbound

MatterSim-MT: A multi-task foundation model for in silico materials characterization cites this paper.

MatterSim-MT: A multi-task foundation model for in silico materials characterization Orb: A Fast, Scalable Neural Network Potential

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:10:52.509332Z

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MatterSim-MT: A multi-task foundation model for in silico materials characterization cites this paper.

MatterSim-MT: A multi-task foundation model for in silico materials characterization Orb: A Fast, Scalable Neural Network Potential

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:35:46.332153Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ccd13a70-2647-488f-892c-16d0e8529da5 · inbound

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models cites this paper.

CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models Orb: A Fast, Scalable Neural Network Potential

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:41:35.697173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85a69fd9-dfe0-491d-9174-b5373f558436 · inbound

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials cites this paper.

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:42:04.525682Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8ed9506c-4910-4e7d-b256-691b832f9b7b · inbound

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates cites this paper.

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates Orb: A Fast, Scalable Neural Network Potential

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:42:03.098388Z

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Observation a24884c9-6563-4f99-86a3-ceffd6a7de6d · inbound

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead cites this paper.

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead Orb: A Fast, Scalable Neural Network Potential

Reference 46

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Source-reported events for the cited work

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Observation 4c3298d1-62bc-48d0-990a-9ab1ee9d0b2e · inbound

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations cites this paper.

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations Orb: A Fast, Scalable Neural Network Potential

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T04:28:05.772501Z

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Observation 988974d6-8a5c-4005-b9a1-d9feda53857b · inbound

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations cites this paper.

Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations Orb: A Fast, Scalable Neural Network Potential

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:34:57.927209Z

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Observation d5df8d75-daab-4923-acc1-1e8a8484d6f2 · inbound

Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI) cites this paper.

Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI) Orb: A Fast, Scalable Neural Network Potential

Reference 21

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f02222d-55fd-4643-9e76-c5e64bf8f30b · inbound

Geometry-based Discovery of Calcium Battery Cathodes Accelerated by Foundational Machine-Learned Models cites this paper.

Geometry-based Discovery of Calcium Battery Cathodes Accelerated by Foundational Machine-Learned Models Orb: A Fast, Scalable Neural Network Potential

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-29T10:43:19.239929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4c005fb8-a53c-41d5-ba37-8046abc32cd6 · inbound

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials cites this paper.

Non-covalent Interactions at cm$^{-1}$ Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials Orb: A Fast, Scalable Neural Network Potential

Reference 16

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab903c60-8524-4137-9ed2-675d40b66448 · inbound

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization cites this paper.

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization Orb: A Fast, Scalable Neural Network Potential

Reference 24

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Observation 83b502dd-7bcf-47b1-b8fb-11ee00a1b0f6 · inbound

Synthetic pre-training of graph-network models for predicting solid-state NMR parameters cites this paper.

Synthetic pre-training of graph-network models for predicting solid-state NMR parameters Orb: A Fast, Scalable Neural Network Potential

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-27T12:30:55.199439Z

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Observation 16aeb085-c87c-4253-83f4-a8dc56372107 · inbound

Fine-tuning MLIP foundation models: strategies for accuracy and transferability cites this paper.

Fine-tuning MLIP foundation models: strategies for accuracy and transferability Orb: A Fast, Scalable Neural Network Potential

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:19.654159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4562a227-8a7c-43aa-98a5-24fbdda440df · 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 Orb: A Fast, Scalable Neural Network Potential

Reference 30

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Observation 816c0e26-f09d-41bb-aecf-ea0b154236ec · inbound

SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery cites this paper.

SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery Orb: A Fast, Scalable Neural Network Potential

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:49:42.333584Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ddd96b36-f1ea-4a0b-889f-763432a761e5 · inbound

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models cites this paper.

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models Orb: A Fast, Scalable Neural Network Potential

Reference 98

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5cdbe53-5bc9-4613-b047-e3467ac15228 · inbound

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design cites this paper.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Orb: A Fast, Scalable Neural Network Potential

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T00:34:05.248660Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 539fc044-441e-43b5-bfa1-02759c521c60 · inbound

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW cites this paper.

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW Orb: A Fast, Scalable Neural Network Potential

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:37:49.002617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 093d2cbd-9dcf-4798-909d-89342ea47030 · inbound

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW cites this paper.

Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW Orb: A Fast, Scalable Neural Network Potential

Reference 39

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Observation 21af87da-f271-4157-af57-c95e806181bb · 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 Orb: A Fast, Scalable Neural Network Potential

Reference 41

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Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models cites this paper.

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models Orb: A Fast, Scalable Neural Network Potential

Reference 28

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Observation 634002fe-e05e-4f2e-a282-80f60e76d136 · inbound

From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions cites this paper.

From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions Orb: A Fast, Scalable Neural Network Potential

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8822d2fa-4666-4858-a0cc-d099440cecb7 · inbound

Quantum machine learning interatomic potential: Application of variational quantum algorithm cites this paper.

Quantum machine learning interatomic potential: Application of variational quantum algorithm Orb: A Fast, Scalable Neural Network Potential

Reference 12

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Observation 1c5d5b2f-4375-4fde-9662-3c9933bc5374 · inbound

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations cites this paper.

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations Orb: A Fast, Scalable Neural Network Potential

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T12:39:55.614956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.